{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# pysamoo: Surrogate-Assisted Multi-objective Optimization" ] }, { "cell_type": "raw", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ "\n", "|python| |license|\n", "\n", "\n", ".. |python| image:: https://img.shields.io/badge/python-3.9-blue.svg\n", " :alt: python 3.6\n", "\n", ".. |license| image:: https://img.shields.io/badge/license-apache-orange.svg\n", " :alt: license apache\n", " :target: https://www.apache.org/licenses/LICENSE-2.0\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In practice, most optimization problems in practice consist of one or multiple **computationally expensive** objective or constraint functions to which special attention must be paid during algorithm design. Most commonly, so-called surrogates (also known as metamodels or simply approximation models) are utilized during optimization to learn from previous evaluations and exploit this knowledge in future iterations. **pysamoo** is an extension of [pymoo](https://pymoo.org) - a comprehensive toolbox for multi-objective optimization - focusing on solving optimization problems with computationally expensive objective or constraint functions.\n", "\n", "Please find the Github repository here: https://github.com/anyoptimization/pysamoo" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![title](_img/surrogate.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Please find an overview of this software documentation below:" ] }, { "cell_type": "raw", "metadata": { "raw_mimetype": "text/restructuredtext", "tags": [] }, "source": [ ".. admonition:: Overview\n", " :class: myOwnStyle\n", "\n", " - `License <#License>`_: GNU Affero General Public License (AGPL).\n", " - `Installation <#Installation>`_: How to install the current release of pysamoo.\n", " - `Algorithms <#Algorithms>`_: An overview of algorithms and their underlying concepts.\n", " - `Usage <#Usage>`_: Instructions and code snippets to execute algorithms.\n", " - `Contact <#Contact>`_: Information to contact the framework's leading developer." ] }, { "cell_type": "raw", "metadata": { "raw_mimetype": "text/restructuredtext", "tags": [] }, "source": [ ".. csv-table:: Algorithms available in pysamoo\n", " :widths: 60, 10, 30, 30, 200\n", " :file: algorithms.csv" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# License" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**GNU Affero General Public License (AGPL)**: The GNU Affero General Public License is a modified version of the ordinary GNU GPL version 3. It has one added requirement: if you run a modified program on a server and let other users communicate with it there, your server must also allow them to download the source code corresponding to the modified version running there." ] }, { "cell_type": "raw", "metadata": { "raw_mimetype": "text/restructuredtext", "tags": [] }, "source": [ ".. warning::\n", "\n", " Please note that pysamoo has a more permissive software license which only allows non-commercial usage. If you intend to use pysamoo for commercial purposes please contact us." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Citation" ] }, { "cell_type": "raw", "metadata": { "raw_mimetype": "text/restructuredtext", "tags": [] }, "source": [ ".. note::\n", "\n", " If you use this framework, we kindly ask you to cite the following paper:" ] }, { "cell_type": "raw", "metadata": { "raw_mimetype": "text/restructuredtext", "tags": [] }, "source": [ "`Julian Blank, & Kalyanmoy Deb. (2022). pysamoo: Surrogate-Assisted Multi-Objective Optimization in Python. `_\n", "\n", "::\n", "\n", " @misc{pysamoo,\n", " title={pysamoo: Surrogate-Assisted Multi-Objective Optimization in Python}, \n", " author={Julian Blank and Kalyanmoy Deb},\n", " year={2022},\n", " eprint={2204.05855},\n", " archivePrefix={arXiv},\n", " primaryClass={cs.NE}\n", " }" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Installation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The most recent STABLE release of pysamoo can be installed by:" ] }, { "cell_type": "raw", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ "\n", ".. code:: bash\n", "\n", " pip install -U pysamoo" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Feel free to experiment with the instable release by:" ] }, { "cell_type": "raw", "metadata": { "raw_mimetype": "text/restructuredtext", "tags": [] }, "source": [ "\n", ".. code:: bash\n", "\n", " pip install -i https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple pysamoo" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Algorithms" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this part of the software documentation, some more words about the algorithms being implemented in the framework shall be said. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Commonly, surrogates -- approximation or interpolation models -- are utilized during optimization to improve the convergence behavior. \n", "First, one shall distinguish between two different types of evaluations: ESEs that require to run the computationally expensive evaluation; and ASEs which is a computationally inexpensive approximation by the surrogate. \n", "Where the overall optimization run is limited by $\\texttt{ESE}^{\\max}$ function evaluation, function calls of ASEs are only considered as algorithmic overhead. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Simple Surrogate Assisted (SSA)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In order to improve the convergence of NSGA-II, the surrogates provide ASEs and let the algorithm look several iterations into the future without any evaluation of ESEs. \n", "The surrogate models are used to create a set of infill solutions as follows: First, NSGA-II is run for $k$ more iterations (starting from the best solutions found so far), returning the solution set $X^{\\texttt{(cand)}}$.\n", "The number of solutions in $X^{\\texttt{(cand)}}$ corresponds to the population size of the algorithm.\n", "After eliminating duplicates in $X^{\\texttt{(cand)}}$, the number of solutions $N$ desired to run using ESEs needs to be selected. The selection first creates $N$ clusters (in the objective space based on $X^{\\texttt{(cand)}}$) using the k-means algorithm and then uses a roulette wheel selection based on the predicted crowding distances. Note that this will introduce a bias towards boundary points as they have been depicted with a crowding distance of infinity.\n", "Altogether, this results in $N$ solutions to be then evaluated using ESEs in this optimization cycle.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", " \"SSA-NSGA-II\"\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## PSAF" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In contrast to most existing surrogate-assisted algorithms, PSAF uses not only the final solution(s) obtained by optimizing the surrogate but the whole *search pattern*. By making use of the search pattern, the exploration-exploitation balance is found by taking the surrogate's accuracy into account. To allow even more flexible exploitation of the surrogate, we propose two phases. First, derive a solution set that is influenced by the surrogate, and second, introduce surrogate bias by optimizing the surrogate for a number of iterations. Both procedures are important to incorporate surrogates into existing methods effectively." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "One of the major challenges when proposing a generalized optimization framework is the number and strictness of assumptions being made. \n", "On the one hand, too many assumptions restrict the applicability; on the other hand, too few assumptions limit the usage of existing elements in algorithms.\n", "In this study, we target any type of population-based algorithm with two phases in an iteration: the process of generating new solutions to be evaluated (infill) and a method processing evaluated infill solutions (advance). \n", "So, how can existing optimization methods be described into *infill* and *advance* phases?\n", "Genetic algorithms (GAs) generate new solutions using evolutionary recombination-mutation operators and then process them using an environmental survival selection operator; PSO methods create new solutions based on a particles' current velocity, personal best, and global best, and process the solutions using a replacement strategy; CMAES samples new solutions from a normal distribution, which is then updated in each iteration. Shown by well-known state-of-the-art algorithms following or being suitable to be implemented in this optimization method design pattern, this seems to be a reasonable assumption to be made for a generic framework. Moreover, it is worth noting that some researchers and practitioners also refer to the pattern as *ask-and-tell* interface." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", " \"SSA-NSGA-II\"\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### $\\alpha$-Phase" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A well-known concept in evolutionary computation to introduce a bias toward more promising solutions is *tournament selection*. An individual from the population has to win a tournament to contribute to the mating process.\n", "The number of competitors ($\\alpha$) balances how greedy the selection procedure will be. On the one hand, a larger value of $\\alpha$ allows only elitist solutions to participate in mating, while a smaller value introduces less selection pressure.\n", "For genetic algorithms, the most frequently used tournament mode is the binary tournament ($\\alpha=2$), which compares a pair of solutions regarding one or multiple metrics. A standard binary tournament implementation for constrained single-objective optimization declares the less infeasible solution as the winner if one or both solutions are infeasible or otherwise the solution with the smaller function value.\n", "\n", "In the context of surrogate assistance, the tournament selection introduces surrogate bias during the generation of new infill solutions.\n", "Whereas in genetic algorithms, evaluated solutions (using ESE) compete with each other during mating selection, in PSAF solutions evaluated on the surrogate (ASE) are compared." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", " \"SSA-NSGA-II\"\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### $\\beta$-Phase" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "While the tournament is an effective concept to incorporate the surrogate's approximation, it is limited by looking only a *single* iteration into the future. To further increase the surrogate's impact, the baseline algorithm is continued to run for $\\beta$ more consecutive iterations on the surrogate's approximations.\n", "Inevitably, the question of how many iterations are suitable arises and indicates the importance of tuning $\\beta$.\n", "Nevertheless, even more critical, how should the algorithm profit from simulating the algorithm on the surrogate?\n", "An inappropriate choice of $\\beta$ will cause the surrogate's optimum to be repeatedly found and will entirely discard the baseline algorithm's default infill procedure. \n", "This also causes a diversity loss of infill solutions and does not account for the surrogate's approximation error. Thus, we propose a probabilistic surrogate-assisted approach that balances the surrogate's impact on the baseline algorithm to address these issues.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "An example with five iterations ($\\beta = 5$) and four infill solutions $X_1$, $X_2$, $X_3$, and $X_4$ is also illustrated in the figure below. Calling the infill function of the baseline algorithm results in five solution sets with four solutions each. When running the algorithm, the assignment takes place, and for instance, $X_1$ has four solutions being the closest to, and $X_4$ has six. The assignment of the closest solution will show cluster-like arrangements and preserve diversity." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", " \"SSA-NSGA-II\"\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For more information please we would like to the corresponding publication:" ] }, { "cell_type": "raw", "metadata": { "raw_mimetype": "text/restructuredtext", "tags": [] }, "source": [ "**Publication**:\n", "\n", "`Julian Blank and Kalyanmoy Deb. 2021. PSAF: a probabilistic surrogate-assisted framework for single-objective optimization. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '21). Association for Computing Machinery, New York, NY, USA, 652–659. `_\n", "\n" ] }, { "cell_type": "raw", "metadata": { "raw_mimetype": "text/restructuredtext", "tags": [] }, "source": [ "**BibTex:**\n", "::\n", "\n", " @inproceedings{10.1145/3449639.3459297,\n", " author = {Blank, Julian and Deb, Kalyanmoy},\n", " title = {PSAF: A Probabilistic Surrogate-Assisted Framework for Single-Objective Optimization},\n", " year = {2021},\n", " isbn = {9781450383509},\n", " publisher = {Association for Computing Machinery},\n", " address = {New York, NY, USA},\n", " url = {https://doi.org/10.1145/3449639.3459297},\n", " doi = {10.1145/3449639.3459297},\n", " booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference},\n", " pages = {652–659},\n", " numpages = {8},\n", " keywords = {simulation optimization, metamodel-based optimization, surrogate-assisted optimization, genetic algorithms, evolutionary computing},\n", " location = {Lille, France},\n", " series = {GECCO '21}\n", " }\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## GPSAF" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, PSAF shall be extended to be suitable to handle multiple objectives and constraints.\n", "GPSAF follows the two-phase concept as PSAF. However, the $\\alpha$-phase and the $\\beta$-phase now have to consider multiple criteria when comparing solutions.\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Extension of PSAF to constrained and multi-objective optimization\n", "- What needs to be modified?\n", " - Multiple Surrogates: One for each constraint, one for each objective\n", " - Solution Comparisons: Instead of comparing only the objective, the constraint satisfaction and Pareto Dominance now need to be considered.\n", " - Exploration vs. Exploitation: The bottle variable rho needs to be redefined." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For single-objective optimization, the $\\alpha$-phase has already been described. There, the comparison of the two solutions is based only on one single objective value. \n", "For the more generic version with constrainAnalogously to PSAF, GPSAF further increases the surrogate's impact by looking $\\beta$ iterations into the future through calling infill *and* advance of the baseline algorithm repetitively.\n", "To obtain the $\\beta$-solution for constrained multi-objective problems, we use a so-called Probabilistic Knockout Tournament (PKT) to select solutions from each cluster with the goal of self-adaptively exploiting surrogates. The goal is to use surrogates more when they provide accurate predictions but use them more carefully when they provide only rough estimations. \n", "Necessary for generalization, PKT also applies to problems with multiple objectives and constraints, often with varying complexities and surrogate errors to be considered.ts and objectives, the winner of each solution pool is determined as follows: if *all* solutions are infeasible, select the least infeasible solution; otherwise, select a non-dominated solution (break ties randomly). For both the constraint and objective values, only ASEs are used. \n", "Otherwise, the $\\alpha$-phase remains the same, including its responsibilities and mechanics." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Analogously to PSAF, GPSAF further increases the surrogate's impact by looking $\\beta$ iterations into the future through calling infill *and* advance of the baseline algorithm repetitively.\n", "To obtain the $\\beta$-solution for constrained multi-objective problems, we use a so-called PKT to select solutions from each cluster with the goal of self-adaptively exploiting surrogates. The goal is to use surrogates more when they provide accurate predictions but use them more carefully when they provide only rough estimations. \n", "Necessary for generalization, PKT also applies to problems with multiple objectives and constraints, often with varying complexities and surrogate errors to be considered." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", " \"SSA-NSGA-II\"\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For more information please we would like to the corresponding publication:" ] }, { "cell_type": "raw", "metadata": { "raw_mimetype": "text/restructuredtext", "tags": [] }, "source": [ "**Publication**:\n", "\n", "`Julian Blank and Kalyanmoy Deb. 2022. GPSAF: A Generalized Probabilistic Surrogate-Assisted Framework for Constrained Single- and Multi-objective Optimization. COINLab Report 202204. `_\n", "\n" ] }, { "cell_type": "raw", "metadata": { "raw_mimetype": "text/restructuredtext", "tags": [] }, "source": [ "**BibTex:**\n", "::\n", "\n", " @misc{gpsaf,\n", " doi = {10.48550/ARXIV.2204.04054},\n", " url = {https://arxiv.org/abs/2204.04054},\n", " author = {Blank, Julian and Deb, Kalyanmoy},\n", " keywords = {Optimization and Control (math.OC), Machine Learning (cs.LG), Mathematical Software (cs.MS), FOS: Mathematics, FOS: Mathematics, FOS: Computer and information sciences, FOS: Computer and information sciences, G.1.6; G.1.2; I.6.3, 68U07},\n", " title = {GPSAF: A Generalized Probabilistic Surrogate-Assisted Framework for Constrained Single- and Multi-objective Optimization},\n", " publisher = {arXiv},\n", " year = {2022},\n", " copyright = {arXiv.org perpetual, non-exclusive license}\n", " }\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Usage" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In general, *pysamoo* uses the main functionalities of pymoo for defining the optimization problem. However, it provides a new set of algorithms designed for computationally expensive functions. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## SSA-NSGA-II" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For bi-objective optimization problems, a variant of NSGA-II called Simple Surrogate Assisted NSGA-II (SSA-NSGA-II) could be a good starting point. " ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "============================================================\n", "n_gen | n_eval | igd | gd | hv \n", "============================================================\n", " 1 | 50 | 1.526442823 | 2.253783461 | 0.00000E+00\n", " 2 | 60 | 0.046396914 | 0.300196586 | 0.595018331\n", " 3 | 70 | 0.033947341 | 0.008993312 | 0.610204749\n", " 4 | 80 | 0.024488745 | 0.010265171 | 0.627719544\n", " 5 | 90 | 0.023731220 | 0.013908376 | 0.630506522\n", " 6 | 100 | 0.020993013 | 0.012455222 | 0.635631372\n", " 7 | 110 | 0.019087553 | 0.012077546 | 0.638694173\n", " 8 | 120 | 0.018454795 | 0.009261683 | 0.639605933\n", " 9 | 130 | 0.017399517 | 0.009701286 | 0.641156067\n", " 10 | 140 | 0.016730949 | 0.012260403 | 0.641573319\n", " 11 | 150 | 0.015736094 | 0.012211167 | 0.643410891\n", " 12 | 160 | 0.014104293 | 0.012119646 | 0.645610005\n", " 13 | 170 | 0.012296655 | 0.010243625 | 0.648737520\n", " 14 | 180 | 0.011535042 | 0.009461263 | 0.650443282\n", " 15 | 190 | 0.010349809 | 0.008950225 | 0.651784655\n", " 16 | 200 | 0.009829221 | 0.009597486 | 0.652961699\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "image/png": { "height": 373, "width": 498 }, "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from pymoo.optimize import minimize\n", "from pymoo.problems.multi.zdt import ZDT1\n", "from pymoo.visualization.scatter import Scatter\n", "from pysamoo.algorithms.ssansga2 import SSANSGA2\n", "\n", "problem = ZDT1(n_var=10)\n", "\n", "algorithm = SSANSGA2(n_initial_doe=50,\n", " n_infills=10,\n", " surr_pop_size=100,\n", " surr_n_gen=50)\n", "\n", "res = minimize(\n", " problem,\n", " algorithm,\n", " ('n_evals', 200),\n", " seed=1,\n", " verbose=True)\n", "\n", "plot = Scatter()\n", "plot.add(problem.pareto_front(), plot_type=\"line\", color=\"black\", alpha=0.7)\n", "plot.add(res.F, facecolor=\"none\", edgecolor=\"red\")\n", "plot.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## PSAF-GA" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "=========================================================================================================\n", "n_gen | n_eval | fopt | fopt_gap | favg | bias | mae | model \n", "=========================================================================================================\n", " 2 | 30 | 2.00067E+01 | 2.00067E+01 | 2.08792E+01 | - | 0.295669478 | RBF[kernel=linear,tail=linear,normalized=True]\n", " 3 | 40 | 1.25358E+01 | 1.25358E+01 | 1.95328E+01 | 0.700000000 | 0.581973574 | RBF[kernel=linear,tail=linear,normalized=True]\n", " 4 | 50 | 1.25358E+01 | 1.25358E+01 | 1.70467E+01 | 0.700000000 | 0.831762378 | RBF[kernel=linear,tail=constant,normalized=True]\n", " 5 | 60 | 1.24920E+01 | 1.24920E+01 | 1.48254E+01 | 0.700000000 | 0.980943312 | RBF[kernel=linear,tail=constant,normalized=True]\n", " 6 | 70 | 1.24920E+01 | 1.24920E+01 | 1.41739E+01 | 0.700000000 | 0.809506177 | krg-cont\n", " 7 | 80 | 7.524701433 | 7.524701433 | 1.17633E+01 | 0.700000000 | 1.061379318 | krg-cont\n", " 8 | 90 | 6.231815096 | 6.231815096 | 8.987145219 | 0.700000000 | 0.739570348 | krg-cont\n", " 9 | 100 | 4.436184698 | 4.436184698 | 6.836947753 | 0.700000000 | 0.688046885 | krg-lin\n", " 10 | 110 | 4.033330927 | 4.033330927 | 5.477620620 | 0.725546230 | 0.620491761 | krg-lin\n", " 11 | 120 | 3.454903145 | 3.454903145 | 4.480489282 | 0.742306740 | 0.659343091 | krg-lin\n", " 12 | 130 | 2.815285833 | 2.815285833 | 3.889942377 | 0.735535953 | 0.614470498 | krg-lin\n", " 13 | 140 | 2.485750595 | 2.485750595 | 3.363607960 | 0.700000000 | 0.627636124 | krg-lin\n", " 14 | 150 | 2.485750595 | 2.485750595 | 3.197909089 | 0.700000000 | 0.790408089 | krg-lin\n", " 15 | 160 | 2.476723582 | 2.476723582 | 2.982454112 | 0.700000000 | 0.867001279 | krg-lin\n", " 16 | 170 | 2.434431373 | 2.434431373 | 2.776993874 | 0.700000000 | 0.845437156 | krg-lin\n", " 17 | 180 | 2.126776496 | 2.126776496 | 2.714992663 | 0.700000000 | 0.729268722 | krg-lin\n", " 18 | 190 | 2.078240035 | 2.078240035 | 2.500405462 | 0.700000000 | 1.806320892 | RBF[kernel=cubic,tail=linear+quadratic,normalized=False]\n", " 19 | 200 | 2.078240035 | 2.078240035 | 2.500405462 | 0.700000000 | 1.321117683 | krg-cont\n", " 20 | 210 | 1.653515436 | 1.653515436 | 2.303239380 | 0.700000000 | 1.264441877 | krg-cont\n", " 21 | 220 | 1.442352553 | 1.442352553 | 2.087226240 | 0.700000000 | 1.294728627 | krg-cont\n", " 22 | 230 | 0.824849738 | 0.824849738 | 1.688611006 | 0.700000000 | 1.240271042 | krg-cont\n", " 23 | 240 | 0.708080126 | 0.708080126 | 1.471188774 | 0.700000000 | 1.233600703 | krg-cont\n", " 24 | 250 | 0.707589803 | 0.707589803 | 1.226136628 | 0.700000000 | 0.493833637 | RBF[kernel=gaussian,tail=constant,normalized=True]\n", " 25 | 260 | 0.707589803 | 0.707589803 | 1.201866513 | 0.700000000 | 1.284121721 | krg-cont\n", " 26 | 270 | 0.707589803 | 0.707589803 | 1.147485885 | 0.700000000 | 1.407075473 | krg-cont\n", " 27 | 280 | 0.520014186 | 0.520014186 | 0.923809691 | 0.700000000 | 1.446130744 | krg-cont\n", " 28 | 290 | 0.518572748 | 0.518572748 | 0.761520652 | 0.700000000 | 1.460985476 | krg-cont\n", " 29 | 300 | 0.434843502 | 0.434843502 | 0.611597245 | 0.700000000 | 1.385878913 | krg-cont\n", "Best solution found: \n", "X = [-0.12688657 -0.00297504 0.04256559 -0.00187105 0.04402346 0.00120705\n", " -0.01615695 0.06042537 -0.10188824 -0.06181256]\n", "F = [0.4348435]\n", "CV=[0.]\n" ] } ], "source": [ "from pymoo.algorithms.soo.nonconvex.ga import GA\n", "from pymoo.optimize import minimize\n", "from pymoo.problems.single import Ackley\n", "from pysamoo.algorithms.psaf import PSAF\n", "\n", "problem = Ackley(n_var=10)\n", "\n", "algorithm = GA(pop_size=20, n_offsprings=10)\n", "\n", "algorithm = PSAF(algorithm, n_initial_doe=30, alpha=10, beta=30, max_rho=0.7, n_max_infills=10, n_max_doe=500)\n", "\n", "res = minimize(\n", " problem,\n", " algorithm,\n", " ('n_evals', 300),\n", " seed=2,\n", " verbose=True)\n", "\n", "print(\"Best solution found: \\nX = %s\\nF = %s\\nCV=%s\" % (res.X, res.F, res.CV))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## PSAF-DE" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "=========================================================================================================\n", "n_gen | n_eval | fopt | fopt_gap | favg | bias | mae | model \n", "=========================================================================================================\n", " 2 | 30 | 2.00067E+01 | 2.00067E+01 | 2.08792E+01 | - | 0.295669478 | RBF[kernel=linear,tail=linear,normalized=True]\n", " 3 | 40 | 1.44799E+01 | 1.44799E+01 | 1.93934E+01 | 0.700000000 | 0.799409015 | RBF[kernel=linear,tail=linear,normalized=True]\n", " 4 | 50 | 1.44799E+01 | 1.44799E+01 | 1.91167E+01 | 0.700000000 | 0.928172336 | RBF[kernel=linear,tail=constant,normalized=True]\n", " 5 | 60 | 1.31103E+01 | 1.31103E+01 | 1.89794E+01 | 0.700000000 | 1.040848676 | RBF[kernel=linear,tail=linear,normalized=True]\n", " 6 | 70 | 1.09561E+01 | 1.09561E+01 | 1.86909E+01 | 0.700000000 | 1.154333194 | krg-cont\n", " 7 | 80 | 9.420417041 | 9.420417041 | 1.83074E+01 | 0.700000000 | 1.180671386 | krg-cont\n", " 8 | 90 | 9.420417041 | 9.420417041 | 1.81428E+01 | 0.700000000 | 0.647627100 | krg-cont\n", " 9 | 100 | 8.365940100 | 8.365940100 | 1.80568E+01 | 0.700000000 | 0.554954396 | krg-cont\n", " 10 | 110 | 8.024922715 | 8.024922715 | 1.78922E+01 | 0.741153019 | 0.527368981 | krg-cont\n", " 11 | 120 | 7.396652392 | 7.396652392 | 1.72090E+01 | 0.825083790 | 0.518345385 | krg-lin\n", " 12 | 130 | 4.868026774 | 4.868026774 | 1.70488E+01 | 0.816386754 | 0.517685002 | krg-lin\n", " 13 | 140 | 4.868026774 | 4.868026774 | 1.67959E+01 | 0.799027499 | 0.519939569 | krg-lin\n", " 14 | 150 | 4.868026774 | 4.868026774 | 1.67142E+01 | 0.744865385 | 0.518127439 | krg-lin\n", " 15 | 160 | 4.868026774 | 4.868026774 | 1.66530E+01 | 0.741296851 | 0.547838549 | krg-lin\n", " 16 | 170 | 4.868026774 | 4.868026774 | 1.66045E+01 | 0.720500231 | 0.540898946 | krg-cont\n", " 17 | 180 | 4.868026774 | 4.868026774 | 1.62857E+01 | 0.728552239 | 0.576995856 | krg-cont\n", " 18 | 190 | 4.868026774 | 4.868026774 | 1.56730E+01 | 0.740742001 | 0.616144531 | krg-cont\n", " 19 | 200 | 4.801632381 | 4.801632381 | 1.55472E+01 | 0.765039254 | 0.760504208 | krg-cont\n", " 20 | 210 | 4.801632381 | 4.801632381 | 1.55067E+01 | 0.700000000 | 0.810652396 | krg-cont\n", " 21 | 220 | 4.801632381 | 4.801632381 | 1.53688E+01 | 0.700000000 | 0.813004751 | krg-lin\n", " 22 | 230 | 4.712859472 | 4.712859472 | 1.51936E+01 | 0.700000000 | 0.760527480 | krg-lin\n", " 23 | 240 | 4.061073431 | 4.061073431 | 1.51177E+01 | 0.700000000 | 0.681421676 | krg-lin\n", " 24 | 250 | 4.061073431 | 4.061073431 | 1.51046E+01 | 0.700000000 | 0.531587588 | krg-lin\n", " 25 | 260 | 3.619382893 | 3.619382893 | 1.49276E+01 | 0.704480041 | 0.388432617 | krg-lin\n", " 26 | 270 | 3.619382893 | 3.619382893 | 1.48554E+01 | 0.827923624 | 0.441110858 | krg-lin\n", " 27 | 280 | 3.535249723 | 3.535249723 | 1.48261E+01 | 0.700000000 | 0.494988780 | krg-lin\n", " 28 | 290 | 3.414219581 | 3.414219581 | 1.47411E+01 | 0.700000000 | 0.510668380 | krg-lin\n", " 29 | 300 | 2.717046992 | 2.717046992 | 1.46942E+01 | 0.700000000 | 0.516918929 | krg-lin\n", "Best solution found: \n", "X = [-0.07750256 -0.63698302 -0.00699189 -0.13305234 -0.71384496 0.12294314\n", " 0.16717794 -0.35720554 0.24511258 -0.14820306]\n", "F = [2.71704699]\n", "CV=[0.]\n" ] } ], "source": [ "from pymoo.algorithms.soo.nonconvex.de import DE\n", "from pymoo.optimize import minimize\n", "from pymoo.problems.single import Ackley\n", "from pysamoo.algorithms.psaf import PSAF\n", "\n", "problem = Ackley(n_var=10)\n", "\n", "algorithm = DE(pop_size=20, n_offsprings=10)\n", "\n", "algorithm = PSAF(algorithm, n_initial_doe=30, alpha=10, beta=30, max_rho=0.7, n_max_infills=10, n_max_doe=500)\n", "\n", "res = minimize(\n", " problem,\n", " algorithm,\n", " ('n_evals', 300),\n", " seed=2,\n", " verbose=True)\n", "\n", "print(\"Best solution found: \\nX = %s\\nF = %s\\nCV=%s\" % (res.X, res.F, res.CV))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## PSAF-CMAES" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "=========================================================================================================\n", "n_gen | n_eval | fopt | fopt_gap | favg | bias | mae | model \n", "=========================================================================================================\n", " 2 | 30 | 2.00067E+01 | 2.00067E+01 | 2.08792E+01 | - | 0.295669478 | RBF[kernel=linear,tail=linear,normalized=True]\n", " 3 | 40 | 1.18365E+01 | 1.18365E+01 | 1.62963E+01 | 0.700000000 | 0.929692620 | RBF[kernel=linear,tail=linear,normalized=True]\n", " 4 | 50 | 1.18365E+01 | 1.18365E+01 | 1.57489E+01 | 0.700000000 | 1.182626727 | RBF[kernel=linear,tail=constant,normalized=True]\n", " 5 | 60 | 1.18365E+01 | 1.18365E+01 | 1.62275E+01 | 0.700000000 | 1.397991439 | RBF[kernel=linear,tail=linear,normalized=True]\n", " 6 | 70 | 1.09457E+01 | 1.09457E+01 | 1.54529E+01 | 0.700000000 | 1.648505909 | krg-cont\n", " 7 | 80 | 6.533311680 | 6.533311680 | 1.07105E+01 | 0.700000000 | 1.825972978 | krg-cont\n", " 8 | 90 | 6.533311680 | 6.533311680 | 1.31478E+01 | 0.700000000 | 1.253685736 | krg-cont\n", " 9 | 100 | 5.536903012 | 5.536903012 | 9.122003134 | 0.700000000 | 0.907588724 | krg-cont\n", " 10 | 110 | 5.536903012 | 5.536903012 | 1.04466E+01 | 0.850476128 | 0.886723286 | krg-cont\n", " 11 | 120 | 3.606023869 | 3.606023869 | 7.930313180 | 0.879655769 | 0.887938147 | krg-cont\n", " 12 | 130 | 3.606023869 | 3.606023869 | 6.775835323 | 0.883996295 | 0.988008018 | krg-cont\n", " 13 | 140 | 3.606023869 | 3.606023869 | 7.405047260 | 0.814311314 | 1.109251424 | krg-cont\n", " 14 | 150 | 3.554418048 | 3.554418048 | 6.533350830 | 0.732990295 | 1.102437071 | krg-cont\n", " 15 | 160 | 3.554418048 | 3.554418048 | 6.835437951 | 0.711012725 | 1.152080594 | krg-lin\n", " 16 | 170 | 3.310282127 | 3.310282127 | 5.174924208 | 0.700000000 | 1.065966219 | krg-lin\n", " 17 | 180 | 2.969732370 | 2.969732370 | 4.697683678 | 0.700000000 | 0.821900196 | krg-lin\n", " 18 | 190 | 2.969732370 | 2.969732370 | 5.369859845 | 0.700000000 | 0.811711587 | krg-lin\n", " 19 | 200 | 2.969732370 | 2.969732370 | 5.051947329 | 0.700000000 | 0.930113008 | krg-lin\n", " 20 | 210 | 2.969732370 | 2.969732370 | 4.527261684 | 0.700000000 | 0.918888950 | krg-cont\n", " 21 | 220 | 2.969732370 | 2.969732370 | 3.876461054 | 0.700000000 | 1.015796645 | krg-cont\n", " 22 | 230 | 2.969732370 | 2.969732370 | 4.094044762 | 0.700000000 | 1.074563507 | krg-cont\n", " 23 | 240 | 2.969732370 | 2.969732370 | 4.221977758 | 0.700000000 | 0.991782291 | krg-cont\n", " 24 | 250 | 2.969732370 | 2.969732370 | 4.075217974 | 0.700000000 | 0.925522867 | krg-cont\n", " 25 | 260 | 2.969732370 | 2.969732370 | 3.731054056 | 0.700000000 | 0.895323496 | krg-lin\n", " 26 | 270 | 2.469264070 | 2.469264070 | 3.499506787 | 0.700000000 | 0.876986526 | krg-lin\n", " 27 | 280 | 1.248383248 | 1.248383248 | 3.334046888 | 0.700000000 | 0.723206719 | RBF[kernel=mq,tail=constant,normalized=True]\n", " 28 | 290 | 1.248383248 | 1.248383248 | 8.795659864 | 0.700000000 | 6.06439E+03 | RBF[kernel=gaussian,tail=quadratic,normalized=True]\n", " 29 | 300 | 1.248383248 | 1.248383248 | 1.04755E+01 | 0.700000000 | 1.622633898 | krg-cont\n", "Best solution found: \n", "X = [ 0.073688 0.12611394 0.21745451 0.05631034 -0.28150535 0.07995362\n", " -0.12594537 0.04513843 -0.06595957 0.05103607]\n", "F = [1.24838325]\n", "CV=[0.]\n" ] } ], "source": [ "from pymoo.algorithms.soo.nonconvex.cmaes import SimpleCMAES\n", "from pymoo.optimize import minimize\n", "from pymoo.problems.single import Ackley\n", "from pysamoo.algorithms.psaf import PSAF\n", "\n", "problem = Ackley(n_var=10)\n", "\n", "algorithm = SimpleCMAES(pop_size=20)\n", "\n", "algorithm = PSAF(algorithm, n_initial_doe=30, alpha=10, beta=30, max_rho=0.7, n_max_infills=10, n_max_doe=500)\n", "\n", "res = minimize(\n", " problem,\n", " algorithm,\n", " ('n_evals', 300),\n", " seed=2,\n", " verbose=True)\n", "\n", "print(\"Best solution found: \\nX = %s\\nF = %s\\nCV=%s\" % (res.X, res.F, res.CV))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## GPSAF-GA" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "=========================================================================================================\n", "n_gen | n_eval | fopt | fopt_gap | favg | n_influenced | mae | model \n", "=========================================================================================================\n", " 2 | 30 | 2.03679E+01 | 2.03679E+01 | 2.11218E+01 | - | 0.239553735 | RBF[kernel=gaussian,tail=quadratic,normalized=True]\n", " 3 | 40 | 1.54071E+01 | 1.54071E+01 | 2.00529E+01 | 2/10 | 0.375380271 | RBF[kernel=gaussian,tail=quadratic,normalized=True]\n", " 4 | 50 | 1.54071E+01 | 1.54071E+01 | 1.85399E+01 | 2/10 | 0.566694558 | RBF[kernel=gaussian,tail=quadratic,normalized=True]\n", " 5 | 60 | 1.50642E+01 | 1.50642E+01 | 1.65616E+01 | 8/10 | 0.788562645 | RBF[kernel=linear,tail=constant,normalized=True]\n", " 6 | 70 | 1.49632E+01 | 1.49632E+01 | 1.55563E+01 | 7/10 | 0.875499159 | RBF[kernel=linear,tail=linear,normalized=True]\n", " 7 | 80 | 1.40722E+01 | 1.40722E+01 | 1.52481E+01 | 2/10 | 1.032806639 | RBF[kernel=linear,tail=constant,normalized=True]\n", " 8 | 90 | 1.40722E+01 | 1.40722E+01 | 1.50859E+01 | 4/10 | 0.850240497 | kriging-lin-ARD\n", " 9 | 100 | 1.11271E+01 | 1.11271E+01 | 1.41018E+01 | 5/10 | 0.852619885 | kriging-lin-ARD\n", " 10 | 110 | 9.862148820 | 9.862148820 | 1.26316E+01 | 5/10 | 0.861746557 | kriging-lin-ARD\n", " 11 | 120 | 9.089850699 | 9.089850699 | 1.07926E+01 | 4/10 | 0.653958805 | kriging-lin-ARD\n", " 12 | 130 | 8.365462379 | 8.365462379 | 9.641716361 | 4/10 | 0.763669813 | kriging-quadr\n", " 13 | 140 | 7.213496632 | 7.213496632 | 9.169209856 | 2/10 | 0.747123340 | kriging-const-ARD\n", " 14 | 150 | 5.518798604 | 5.518798604 | 8.311198673 | 2/10 | 0.696688095 | kriging-const-ARD\n", " 15 | 160 | 5.166341432 | 5.166341432 | 7.250085359 | 4/10 | 0.745274001 | kriging-const-ARD\n", " 16 | 170 | 4.715293949 | 4.715293949 | 6.190406085 | 3/10 | 0.851376307 | kriging-const-ARD\n", " 17 | 180 | 4.506389436 | 4.506389436 | 5.441595504 | 3/10 | 0.880704806 | kriging-const-ARD\n", " 18 | 190 | 3.602899195 | 3.602899195 | 5.080318965 | 5/10 | 0.832961294 | kriging-const-ARD\n", " 19 | 200 | 3.449609475 | 3.449609475 | 4.829779206 | 8/10 | 0.582696071 | kriging-quadr\n", " 20 | 210 | 2.896292947 | 2.896292947 | 4.326263670 | 3/10 | 0.954391856 | kriging-const-ARD\n", " 21 | 220 | 2.875140994 | 2.875140994 | 3.842885253 | 5/10 | 0.895850569 | kriging-const-ARD\n", " 22 | 230 | 2.875140994 | 2.875140994 | 3.630956323 | 7/10 | 0.980938095 | kriging-const-ARD\n", " 23 | 240 | 2.875140994 | 2.875140994 | 3.408055362 | 8/10 | 0.628303629 | kriging-const\n", " 24 | 250 | 1.997437256 | 1.997437256 | 3.178345845 | 5/10 | 0.596562419 | kriging-const\n", " 25 | 260 | 1.997437256 | 1.997437256 | 3.019568595 | 6/10 | 0.355156925 | kriging-quadr\n", " 26 | 270 | 1.997437256 | 1.997437256 | 2.914276289 | 9/10 | 0.521409800 | kriging-quadr\n", " 27 | 280 | 1.891430539 | 1.891430539 | 2.617365610 | 6/10 | 0.946184555 | kriging-const\n", " 28 | 290 | 1.891430539 | 1.891430539 | 2.480695613 | 7/10 | 1.101435095 | kriging-const\n", " 29 | 300 | 1.686540303 | 1.686540303 | 2.243963562 | 5/10 | 1.263357719 | kriging-const\n", "Best solution found: \n", "X = [-0.01017884 0.11274214 0.15408955 -0.04909597 -0.2538259 -0.07108236\n", " 0.82884658 -0.00939967 0.0244004 -0.05127961]\n", "F = [1.6865403]\n", "CV=[0.]\n" ] } ], "source": [ "from pymoo.algorithms.soo.nonconvex.ga import GA\n", "from pymoo.optimize import minimize\n", "from pymoo.problems.single import Ackley\n", "from pysamoo.algorithms.gpsaf import GPSAF\n", "\n", "problem = Ackley(n_var=10)\n", "\n", "algorithm = GA(pop_size=20, n_offsprings=10)\n", "\n", "algorithm = GPSAF(algorithm, n_initial_doe=30, alpha=10, beta=30, n_max_infills=10, n_max_doe=500)\n", "\n", "res = minimize(\n", " problem,\n", " algorithm,\n", " ('n_evals', 300),\n", " seed=2,\n", " verbose=True)\n", "\n", "print(\"Best solution found: \\nX = %s\\nF = %s\\nCV=%s\" % (res.X, res.F, res.CV))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## GPSAF-DE" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "=========================================================================================================\n", "n_gen | n_eval | fopt | fopt_gap | favg | n_influenced | mae | model \n", "=========================================================================================================\n", " 2 | 30 | 2.03679E+01 | 2.03679E+01 | 2.11218E+01 | - | 0.239553735 | RBF[kernel=gaussian,tail=quadratic,normalized=True]\n", " 3 | 40 | 1.78125E+01 | 1.78125E+01 | 2.07458E+01 | 6/10 | 0.509548028 | RBF[kernel=gaussian,tail=quadratic,normalized=False]\n", " 4 | 50 | 1.68242E+01 | 1.68242E+01 | 2.05594E+01 | 4/10 | 0.685218247 | RBF[kernel=gaussian,tail=quadratic,normalized=False]\n", " 5 | 60 | 1.41108E+01 | 1.41108E+01 | 2.01638E+01 | 7/10 | 0.809356002 | RBF[kernel=linear,tail=linear,normalized=True]\n", " 6 | 70 | 1.35749E+01 | 1.35749E+01 | 2.01384E+01 | 4/10 | 0.983685938 | RBF[kernel=gaussian,tail=quadratic,normalized=False]\n", " 7 | 80 | 1.08758E+01 | 1.08758E+01 | 1.97375E+01 | 8/10 | 1.288386170 | RBF[kernel=gaussian,tail=quadratic,normalized=False]\n", " 8 | 90 | 1.08758E+01 | 1.08758E+01 | 1.96707E+01 | 6/10 | 0.631725721 | kriging-lin\n", " 9 | 100 | 1.08758E+01 | 1.08758E+01 | 1.93563E+01 | 8/10 | 0.672191691 | kriging-lin\n", " 10 | 110 | 1.08758E+01 | 1.08758E+01 | 1.91950E+01 | 8/10 | 0.627515741 | kriging-lin\n", " 11 | 120 | 9.820629978 | 9.820629978 | 1.90045E+01 | 8/10 | 0.600822520 | kriging-const\n", " 12 | 130 | 7.571886452 | 7.571886452 | 1.85637E+01 | 8/10 | 0.525814310 | kriging-const-ARD\n", " 13 | 140 | 7.256897722 | 7.256897722 | 1.83942E+01 | 7/10 | 0.572336042 | kriging-lin\n", " 14 | 150 | 7.256897722 | 7.256897722 | 1.80074E+01 | 6/10 | 0.519187690 | kriging-lin\n", " 15 | 160 | 7.256897722 | 7.256897722 | 1.77815E+01 | 6/10 | 0.507572174 | kriging-lin\n", " 16 | 170 | 5.121337727 | 5.121337727 | 1.76856E+01 | 6/10 | 0.540304131 | kriging-lin\n", " 17 | 180 | 5.121337727 | 5.121337727 | 1.76234E+01 | 6/10 | 0.408079937 | kriging-quadr\n", " 18 | 190 | 5.119648172 | 5.119648172 | 1.76234E+01 | 7/10 | 0.652695011 | kriging-lin\n", " 19 | 200 | 5.119648172 | 5.119648172 | 1.75096E+01 | 9/10 | 0.596740525 | kriging-lin-ARD\n", " 20 | 210 | 5.119648172 | 5.119648172 | 1.74706E+01 | 8/10 | 0.764295217 | kriging-lin-ARD\n", " 21 | 220 | 5.119648172 | 5.119648172 | 1.73236E+01 | 9/10 | 0.847162066 | kriging-lin-ARD\n", " 22 | 230 | 5.119648172 | 5.119648172 | 1.72700E+01 | 7/10 | 0.894036590 | kriging-lin-ARD\n", " 23 | 240 | 5.119648172 | 5.119648172 | 1.72254E+01 | 7/10 | 0.549283389 | kriging-quadr-ARD\n", " 24 | 250 | 5.119648172 | 5.119648172 | 1.71836E+01 | 8/10 | 1.057122741 | kriging-lin-ARD\n", " 25 | 260 | 5.119648172 | 5.119648172 | 1.71545E+01 | 6/10 | 0.980904945 | kriging-lin-ARD\n", "BIASED: TOO CLOSE (SKIP)\n", " 26 | 270 | 5.119648172 | 5.119648172 | 1.70505E+01 | 8/10 | 0.941280543 | kriging-lin-ARD\n", " 27 | 280 | 4.709865701 | 4.709865701 | 1.69467E+01 | 6/10 | 0.882364065 | kriging-lin-ARD\n", " 28 | 290 | 4.315938952 | 4.315938952 | 1.68187E+01 | 9/10 | 0.832540588 | kriging-lin-ARD\n", " 29 | 300 | 4.158064584 | 4.158064584 | 1.67729E+01 | 9/10 | 0.608495736 | kriging-quadr-ARD\n", "Best solution found: \n", "X = [-0.01065628 0.0958028 -0.51572614 -0.19052951 0.15756506 -0.92264625\n", " 1.68625079 -0.10290413 -1.11300629 0.49102388]\n", "F = [4.15806458]\n", "CV=[0.]\n" ] } ], "source": [ "from pymoo.algorithms.soo.nonconvex.de import DE\n", "from pymoo.optimize import minimize\n", "from pymoo.problems.single import Ackley\n", "from pysamoo.algorithms.gpsaf import GPSAF\n", "\n", "problem = Ackley(n_var=10)\n", "\n", "algorithm = DE(pop_size=20, n_offsprings=10)\n", "\n", "algorithm = GPSAF(algorithm, n_initial_doe=30, alpha=10, beta=30, n_max_infills=10, n_max_doe=500)\n", "\n", "res = minimize(\n", " problem,\n", " algorithm,\n", " ('n_evals', 300),\n", " seed=2,\n", " verbose=True)\n", "\n", "print(\"Best solution found: \\nX = %s\\nF = %s\\nCV=%s\" % (res.X, res.F, res.CV))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## GPSAF-ISRES" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "=========================================================================================================\n", "n_gen | n_eval | cv (min) | cv (avg) | fopt | fopt_gap | favg | n_influenced\n", "=========================================================================================================\n", " 2 | 27 | 1.90500E+01 | 6.24216E+01 | - | - | - | -\n", " 3 | 28 | 0.00000E+00 | 6.01923E+01 | -3.94366E+00 | 1.10563E+01 | -3.94366E+00 | 1/1\n", " 4 | 29 | 0.00000E+00 | 5.81167E+01 | -6.28168E+00 | 8.718321045 | -5.11267E+00 | 1/1\n", " 5 | 30 | 0.00000E+00 | 5.50714E+01 | -6.28168E+00 | 8.718321045 | -5.37742E+00 | 1/1\n", " 6 | 31 | 0.00000E+00 | 5.13344E+01 | -6.55996E+00 | 8.440040515 | -5.67306E+00 | 1/1\n", " 7 | 32 | 0.00000E+00 | 4.93399E+01 | -7.88559E+00 | 7.114409437 | -6.11556E+00 | 1/1\n", " 8 | 33 | 0.00000E+00 | 4.74805E+01 | -1.28810E+01 | 2.118956391 | -7.24314E+00 | 1/1\n", " 9 | 34 | 0.00000E+00 | 4.43963E+01 | -1.30277E+01 | 1.972319276 | -8.06951E+00 | 1/1\n", " 10 | 35 | 0.00000E+00 | 4.15136E+01 | -1.41539E+01 | 0.846056592 | -8.83006E+00 | 1/1\n", " 11 | 36 | 0.00000E+00 | 3.87791E+01 | -1.46778E+01 | 0.322163692 | -9.47981E+00 | 1/1\n", " 12 | 37 | 0.00000E+00 | 3.72407E+01 | -1.48420E+01 | 0.157984600 | -1.00160E+01 | 1/1\n", " 13 | 38 | 0.00000E+00 | 3.44313E+01 | -1.49503E+01 | 0.049743053 | -1.04646E+01 | 1/1\n", " 14 | 39 | 0.00000E+00 | 3.14390E+01 | -1.49771E+01 | 0.022919358 | -1.08406E+01 | 1/1\n", " 15 | 40 | 0.00000E+00 | 3.07705E+01 | -1.49847E+01 | 0.015345104 | -1.11594E+01 | 1/1\n", " 16 | 41 | 0.00000E+00 | 2.79306E+01 | -1.49956E+01 | 0.004419555 | -1.14334E+01 | 1/1\n", " 17 | 42 | 0.00000E+00 | 2.45578E+01 | -1.49980E+01 | 0.002028637 | -1.16711E+01 | 1/1\n", " 18 | 43 | 0.00000E+00 | 2.20519E+01 | -1.49992E+01 | 0.000781034 | -1.18791E+01 | 1/1\n", " 19 | 44 | 0.00000E+00 | 1.99841E+01 | -1.49997E+01 | 0.000285144 | -1.20626E+01 | 1/1\n", " 20 | 45 | 0.00000E+00 | 1.80961E+01 | -1.49999E+01 | 0.000122219 | -1.22258E+01 | 1/1\n", " 21 | 46 | 0.00000E+00 | 1.66403E+01 | -1.49999E+01 | 0.000050135 | -1.23718E+01 | 1/1\n", " 22 | 47 | 0.00000E+00 | 1.66403E+01 | -1.50000E+01 | 0.000023230 | -1.29537E+01 | 1/1\n", " 23 | 48 | 0.00000E+00 | 1.41835E+01 | -1.50000E+01 | 0.000012194 | -1.30560E+01 | 1/1\n", " 24 | 49 | 0.00000E+00 | 1.20123E+01 | -1.50000E+01 | 5.68877E-06 | -1.31486E+01 | 1/1\n", " 25 | 50 | 0.00000E+00 | 1.07047E+01 | -1.50000E+01 | 3.06274E-06 | -1.32328E+01 | 1/1\n", "Best solution found: \n", "X = [0.99999997 0.99999984 0.99999999 0.99999999 0.99999997 0.99999996\n", " 0.9999999 0.99999999 0.99999977 2.99999944 2.99999964 2.99999968\n", " 0.99999965]\n", "F = [-14.99999694]\n", "CV=[0.]\n" ] } ], "source": [ "from pymoo.algorithms.soo.nonconvex.isres import ISRES\n", "from pymoo.factory import get_problem\n", "from pymoo.optimize import minimize\n", "from pysamoo.algorithms.gpsaf import GPSAF\n", "\n", "problem = get_problem(\"g1\")\n", "\n", "algorithm = ISRES()\n", "\n", "algorithm = GPSAF(algorithm,\n", " alpha=3,\n", " beta=30,\n", " n_max_infills=1)\n", "\n", "res = minimize(\n", " problem,\n", " algorithm,\n", " ('n_evals', 50),\n", " seed=1,\n", " verbose=True)\n", "\n", "print(\"Best solution found: \\nX = %s\\nF = %s\\nCV=%s\" % (res.X, res.F, res.CV))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## GPSAF-NSGA-II" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "=========================================================================================================\n", "n_gen | n_eval | igd | gd | hv | n_influenced | mae f1 | mae f2 \n", "=========================================================================================================\n", " 2 | 21 | 1.894282953 | 3.009591283 | 0.00000E+00 | - | 3.29840E-16 | 0.164848978\n", " 3 | 31 | 0.412635166 | 2.581434100 | 0.204208568 | 2/10 | 2.43659E-16 | 0.149911658\n", " 4 | 41 | 0.397959207 | 0.232942755 | 0.204208568 | 1/10 | 2.71623E-16 | 0.110983079\n", " 5 | 51 | 0.377478684 | 0.096617472 | 0.319462956 | 3/10 | 2.58404E-16 | 0.080359966\n", " 6 | 61 | 0.153897514 | 0.100163899 | 0.415505238 | 3/10 | 2.60278E-16 | 0.071000802\n", " 7 | 71 | 0.145908916 | 0.105243022 | 0.433244159 | 4/10 | 2.87754E-16 | 0.086973328\n", " 8 | 81 | 0.080839003 | 0.068071184 | 0.552468302 | 6/10 | 3.04136E-16 | 0.088093049\n", " 9 | 91 | 0.069593310 | 0.062332489 | 0.568189250 | 5/10 | 6.52689E-16 | 0.088370268\n", " 10 | 101 | 0.057377323 | 0.057706437 | 0.578512036 | 8/10 | 8.92850E-16 | 0.094255146\n", " 11 | 111 | 0.047998378 | 0.054915196 | 0.584751052 | 6/10 | 8.81770E-16 | 0.074656043\n", " 12 | 121 | 0.043389217 | 0.037992011 | 0.591019879 | 7/10 | 1.00372E-15 | 0.071401482\n", " 13 | 131 | 0.041928923 | 0.053218105 | 0.597121025 | 10/10 | 9.74394E-16 | 0.060917258\n", " 14 | 141 | 0.041584411 | 0.052012254 | 0.598180936 | 7/10 | 6.58766E-16 | 0.053754620\n", " 15 | 151 | 0.039302001 | 0.032352983 | 0.601876373 | 4/10 | 4.61768E-16 | 0.055956648\n", " 16 | 161 | 0.037368793 | 0.029728139 | 0.605027282 | 5/10 | 4.15984E-16 | 0.052962000\n", " 17 | 171 | 0.036161438 | 0.028003904 | 0.608097471 | 5/10 | 4.03878E-16 | 0.040439293\n", " 18 | 181 | 0.037280447 | 0.028121721 | 0.606421241 | 6/10 | 3.78968E-16 | 0.040805845\n", " 19 | 191 | 0.035306919 | 0.024778587 | 0.608897397 | 8/10 | 6.32584E-16 | 0.037263158\n", " 20 | 201 | 0.033917625 | 0.024719113 | 0.610046729 | 6/10 | 7.75075E-16 | 0.027292017\n", " 21 | 211 | 0.031875493 | 0.023064972 | 0.614829761 | 7/10 | 9.84907E-16 | 0.028629215\n", " 22 | 221 | 0.030696483 | 0.021607031 | 0.616614582 | 5/10 | 1.03511E-15 | 0.027269083\n", " 23 | 231 | 0.030268446 | 0.020849562 | 0.617555775 | 4/10 | 1.19047E-15 | 0.021726719\n", " 24 | 241 | 0.029651483 | 0.020283724 | 0.618179841 | 7/10 | 9.35363E-16 | 0.017941495\n", " 25 | 251 | 0.028997101 | 0.019134586 | 0.619453784 | 8/10 | 9.57914E-16 | 0.019836130\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "image/png": { "height": 372, "width": 498 }, "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "\n", "from pymoo.algorithms.moo.nsga2 import NSGA2\n", "from pymoo.optimize import minimize\n", "from pymoo.problems.multi import ZDT1\n", "from pymoo.visualization.scatter import Scatter\n", "from pysamoo.algorithms.gpsaf import GPSAF\n", "\n", "problem = ZDT1(n_var=10)\n", "\n", "algorithm = NSGA2(pop_size=20, n_offsprings=10)\n", "\n", "algorithm = GPSAF(algorithm,\n", " alpha=10,\n", " beta=50,\n", " n_max_doe=100,\n", " n_max_infills=np.inf,\n", " )\n", "\n", "res = minimize(\n", " problem,\n", " algorithm,\n", " ('n_evals', 250),\n", " seed=1,\n", " verbose=True)\n", "\n", "plot = Scatter()\n", "plot.add(problem.pareto_front(), plot_type=\"line\", color=\"black\", alpha=0.7)\n", "plot.add(res.F, facecolor=\"none\", edgecolor=\"red\")\n", "plot.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## GPSAF-NSGA-III" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "=========================================================================================================\n", "n_gen | n_eval | cv (min) | cv (avg) | igd | gd | hv | n_influenced\n", "=========================================================================================================\n", " 2 | 5 | 0.285851865 | 3.667450918 | - | - | - | -\n", " 3 | 15 | 0.00000E+00 | 1.257129104 | 0.225786192 | 0.072446443 | 0.147801532 | 5/10\n", " 4 | 25 | 0.00000E+00 | 0.025984099 | 0.149069781 | 0.040586016 | 0.148884568 | 8/10\n", " 5 | 35 | 0.00000E+00 | 0.00000E+00 | 0.114349341 | 0.059066311 | 0.118412630 | 6/10\n", " 6 | 45 | 0.00000E+00 | 0.00000E+00 | 0.112884000 | 0.071252148 | 0.145186502 | 7/10\n", " 7 | 55 | 0.00000E+00 | 0.00000E+00 | 0.116813884 | 0.071035440 | 0.149460183 | 6/10\n", " 8 | 65 | 0.00000E+00 | 0.00000E+00 | 0.110036245 | 0.066122903 | 0.154135607 | 4/10\n", " 9 | 75 | 0.00000E+00 | 0.00000E+00 | 0.079695460 | 0.060042387 | 0.205378897 | 6/10\n", " 10 | 85 | 0.00000E+00 | 0.00000E+00 | 0.084115182 | 0.036462115 | 0.197525401 | 6/10\n", " 11 | 95 | 0.00000E+00 | 0.00000E+00 | 0.075153945 | 0.057117098 | 0.193915686 | 6/10\n", " 12 | 105 | 0.00000E+00 | 0.00000E+00 | 0.073263644 | 0.051969922 | 0.204705373 | 5/10\n", " 13 | 115 | 0.00000E+00 | 0.00000E+00 | 0.077497745 | 0.051136372 | 0.190771392 | 8/10\n", " 14 | 125 | 0.00000E+00 | 0.00000E+00 | 0.054818633 | 0.024302768 | 0.213750172 | 6/10\n", " 15 | 135 | 0.00000E+00 | 0.00000E+00 | 0.057263700 | 0.040403343 | 0.202395732 | 5/10\n", " 16 | 145 | 0.00000E+00 | 0.00000E+00 | 0.072791697 | 0.036278619 | 0.191543860 | 5/10\n", " 17 | 155 | 0.00000E+00 | 0.00000E+00 | 0.053593653 | 0.033452347 | 0.232890076 | 5/10\n", " 18 | 165 | 0.00000E+00 | 0.00000E+00 | 0.064325401 | 0.033864774 | 0.220397000 | 5/10\n", " 19 | 175 | 0.00000E+00 | 0.00000E+00 | 0.053553995 | 0.036168487 | 0.224564328 | 7/10\n", " 20 | 185 | 0.00000E+00 | 0.00000E+00 | 0.050583172 | 0.034369023 | 0.240656107 | 6/10\n", " 21 | 195 | 0.00000E+00 | 0.00000E+00 | 0.049968396 | 0.036422578 | 0.241398332 | 6/10\n", " 22 | 205 | 0.00000E+00 | 0.00000E+00 | 0.049716035 | 0.036072472 | 0.243901757 | 3/10\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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/rfrae+6JZ6B3WHbspJPi/83LzJgRj5b4+mv44Yd41MU115Sfv+mmeOi/JEmSVI0Q1WRGaWUshDB+4MCBA8eXzc6tehFFEQ8//DD/+te/qPjd3n333Rk+fDiF334bz6S+0UbQv3/d3+jHH+M1zdu2jXvdW+TQ71vvvVf+48GTT8Iee1R93bJl8brl338fXz9uXIOV2GhV/GFn8eLyJfCWd8st5ZPk9esHkybVf22SJElKzKBBg3j//fffTy2HXSs5lCSkVQshcMABBzBy5Ei6du2aPv70009z+umnM72gAPbeO7NADvEkchtvHIfWXArkAL/8JfTsGT/fe2944IEVr5k5E/r2jQM5wLXXNlx9jVXF+8c32KD6QA7xHAZlPnThBkmSJFUvx9KEVDObbropN9xwA0OGDEkf+/LLLznppJN45ZVXaPYjRN55J75XftkyOOgg6NwZ/vAHOOwwGDgwvj96ypT42hEj4uH/zd3VV5c///jjVV+f6Q8/kiRJahYM5Wqy2rdvz7nnnstf//pXClKTmy1evJhRo0Zx3XXXsWjRooQrTFDnzlBUBAMGxPs//QT//jf885/wwQfl65jfdhucd16ipTYaP/xQ/nxlveRlKozUkCRJkqpjKFeTFkLgd7/7Hddccw1rrbVW+vhrr73GiSeeyBdffJFgdQnr1AkmTICvvoL99oPeveNh2VtsEU/wNn8+HHlk0lU2HvvtV/78yitXff3YsfVXiyRJkpoMJ3prIE70lrzFixdz22238fLLL6eP5efn8+c//5ndd9+dUNXs7FJFZd+RFi3iWddrcm1BQfmycpIkSWqSnOhNqoFWrVpx4okncuqpp9KqVSsASkpKuOOOO7j00kuZO3duwhWq0Stbi3zZsnjN++qkvl8APPxw/dYkSZKknGYoV7Pz61//muuvv54NN9wwfWzcuHEcf/zxTHLpKq3MtGnlz//7XygshEceKT920EFxD3nZ2uSFhbDXXg1boyRJknKKoVzN0i9+8Quuuuoq9qiwRvfs2bM577zzuO+++ygpKUmwOjVaLVvCq6+W7y9dCvvvHwfxECovL5eXVx7OJUmSpGoYytVsFRQUcMQRR3DBBRfQoUMHAKIo4pFHHuHMM89k+vTpCVeoRmn77WHGDEh9Z6o0cCD4w44kSZJqwFCuZm+LLbbgxhtvZLPNNksfmzp1KieccAJPP/20a5prRauvDnPmxEvHnXgibLYZbL013H9/fMwJHXPfJ5/AKafAGWfEywdKkiTVE2dfbyDOvt74RVHE448/zv33319p+Hr//v056aSTWH311ROsTlKD2GwzmDix6nM77ACvvNKg5UiSpNzg7OtSFoQQ2Geffbjuuuvo0aNH+vikSZM47rjjePXVV+01l5qqxYvjeQGqC+QQzyfQwr82JUlSdvmvC2k5PXr04Nprr2W//fZLr12+cOFCrrvuOi6//HKXTpOaojZtKu9vt118K0IUQb9+5cejKJ7ET5IkKUsM5VIVCgoKOOSQQ7jyyivp3r17+vjbb7/Nsccey5gxYxKsTlJW/eY3cdiGOJxHEbz+evn5SZMqh/Fly+CCCxq8TEmS1DR5T3kD8Z7y3LV48WLuuusunn/++UrHt956a4466ig6d+6cUGWSsiI1IgYoD+erujaEOJxLkiThPeVSvWrVqhXHHHMMF110UaUA/vbbb3PMMcfwwgsveK+51BT06bPqa8qWwvP/85IkKUsM5VINDRo0iL///e/svPPO6WMLFizg73//O2eddRZff/11gtVJqpMJE8qfv//+qq8/9dR6K0WSJDVPhnKpFtq1a8fxxx/PZZddVule848//pgTTjiB+++/n+Li4gQrlFQrnTqVP//551VfP3NmfVUiSZKaKUO5VAf9+/fnpptuYr/99iMvNflTSUkJDz/8MMcddxzjxo1zSLuUCyosf0ivXqu+/pZb6q0USZLUPBnKpToqLCzkkEMOYdSoUfTu3Tt9/LvvvmPEiBFcfPHFTJ8+PcEKJdVI2eRtc+fG65VXp6gISkri54WF9V6WJElqHgzlUoZ69OjBlVdeyTHHHEO7du3Sx8ePH89xxx3HXXfdxbx58xKsUNJKXXRR+fPWrasO5kVFsP765fuvvFLfVUmSpGbCJdEaiEuiNQ9z587lvvvu48UXX6w0fL1169bsscce7LHHHpWCu6RGokMHqPjjWV4eDB0KS5fCmDGVZ1tfZx2YNq3ha5QkSY1WJkuiGcobiKG8efnyyy+59dZbmTJlSqXjbdu2Zc8992T33Xenbdu2CVUnqUqdOsGcOSu/Zr314l5zSZKkClynXGpkNthgA6644grOPPNM1llnnfTxBQsW8MADD3DYYYdxzz338HNNZnuW1DB+/hnGjq36fvE2beB//zOQS5KkrLOnvIHYU958LVu2jNGjR/Ovf/1rhYnfCgsL2Wmnndh7771ZY401EqpQkiRJUiYy6SnPr4+CJJVr0aIF2267Lb/61a944403eOSRR/jmm28AKC4u5rnnnuOFF15g2223ZdiwYfTu3ZtQNhu0JEmSpCbNUC41kLy8PLbffnt+/etfM2bMGB599FE+++wzIO5Nf/3113n99dfp3r07O+ywA9tvv72955IkSVIT5/D1BuLwdS0viiImTpzIo48+ysSJE6u8pm/fvuywww786le/ok2bNg1coSRJkqSacPb1HGAo18p89tlnvPjii7z55pssXLhwhfOFhYVstdVWbLnllvTr14+OHTsmUKUkSZKkqhjKc4ChXDVRXFzM2LFjeeWVV3j//fep7v+fPXr0oH///vTv35++ffu6vJokSZKUICd6k5qIwsJCttlmG7bZZht++uknXn/9dV599VWKlluGqaioiKKiIv7zn/8QQmCjjTaiX79+9OvXj06dOpGXl0d+fj4tWrQgPz+fvLy8So+Kx5xUTpIkSUqOPeUNxJ5yZeLLL79kzJgxTJw4kalTp1JaWpq1tkMIKwT3VQX5ulxb1Xss/6PByt6jNtdWfO6PDpIkSapv9pRLTdwGG2zABhtswIEHHsjixYv5+OOPmTRpEpMmTeLzzz+vdph7TURRxNKlS1m6dGkWK248QgirDPdVjSjI5IeA2rRX3bU1/aEjhOAPD5IkSTnMUC7lmFatWjFw4EAGDhwIwIIFC5g8eTIffvghU6dOpbi4mNLSUkpLSykpKUk/r+5YUxdFESUlJZSUlLBkyZKky6kXNb1VIVs/BGT7h4mVjY7wFgtJktTUGcqlHNe2bVuGDBnCkCFDav3aKIpYtmzZKoN7SUkJy5Ytq3S8qsC//LVVva6696nu2pr8sLCyupuDss9ZXFyccCX1o6rQnuQIhby8PNq3b8+AAQNo0aJF0v95JElSjjOUS81YxaHdTdXyPzpk8kNAbX6YqMl7VNVebX+YaA7zgjTWUR1bbrklZ599tsFckiRlxFAuqUlr0aIFLVq0oKCgIOlS6kUURQ3+Q0BdRlXUpp6K1yxbtizp/8TVeuedd7jjjjs48sgjHWIvSZLqzFAuSTmsbPb8/Pym+cd52Y8Otb19IZMfAlbV3uzZs/nwww8BeOaZZ1hjjTXYc889k/0PJUmSclbT/FecJKlJaIw/OkRRxJVXXsno0aMBuPPOO+natStDhw5NuDJJkpSLvBFOkqRaCCFw8skn06dPn/Sxa6+9lqlTpyZYlSRJylWGckmSaqmwsJDzzz+ftdZaC4ClS5dy1VVXsXDhwoQrkyRJucZQLklSHbRv354LL7yQNm3aAPD9999z2223JVyVJEnKNYZySZLqqHv37hx77LHp/VdffZU33ngjwYokSVKuMZRLkpSBbbfdlu233z69f/PNNzNjxowEK5IkSbnEUC5JUob++te/suaaawKwYMECrrvuuka9xrokSWo8DOWSJGWoTZs2nHbaabRoEf+1OnnyZJ566qmEq5IkSbnAUC5JUhb06tWLP/zhD+n9e++9l6KiouQKkiRJOcFQLklSluy///707NkTgJKSEq655hqWLl2acFWSJKkxM5RLkpQl+fn5nHLKKRQWFgJQVFTEv/71r4SrkiRJjZmhXJKkLFp77bUZPnx4ev+xxx7j888/T64gSZLUqBnKJUnKst12240BAwYAEEURN954I6WlpQlXJUmSGiNDuSRJWRZC4Nhjj00PY//yyy/5z3/+k3BVkiSpMTKUS5JUD7p3784f//jH9P4DDzzADz/8kGBFkiSpMTKUS5JUT/bcc0969OgBwJIlS7jllluIoijZoiRJUqNiKJckqZ7k5+dz/PHHE0IAYPz48bz55psJVyVJkhoTQ7kkSfVo4403Zrfddkvv33777cybNy/BiiRJUmNiKJckqZ4dfPDBdO3aFYA5c+Zw3333JVyRJElqLAzlkiTVs9atW3PUUUel91944QU+++yzBCuSJEmNhaFckqQGMGTIEH75y18C8drlTvomSZLAUC5JUoMIIXDkkUdSUFAAwGeffcaLL76YcFWSJClphnJJkhpI9+7d2WeffdL79957L3Pnzk2wIkmSlDRDuSRJDWi//fZjjTXWAGDevHncc889CVckSZKSZCiXJKkBFRYWVpr07b///S+ff/55/b7pscdCCJUfJ55Yv+8pSZJqxFAuSVID22KLLdhiiy2AeNK322+/vX4mfSsL4zffvOK5G24wnEuS1AgYyiVJSsDhhx9Ofn4+AFOmTOHNN9/M7hvst1/VYXx5N9wABxyQ3feWJEk1ZiiXJCkBa621Frvvvnt6/6677mLx4sXZaXz6dHj00fL9XXeFKKr8+O1vy88//HD8GkmS1OAM5ZIkJeSAAw6gU6dOAMyaNYtHKwbpTPTsWf585Eh47rkVr3nhBTj//PL9jTfOzntLkqRaMZRLkpSQNm3acOihh6b3H3/8cX744YfMG160qPz5WWdVf90ll5Q/X7gw8/eVJEm1ZiiXJClBO+64Iz1TPdtLly7lrrvuyl7jRx656msOOSR77ydJkmrNUC5JUoJCCBxZITy//fbbTJo0qe4NVuwlP/30VV//l7/U/b0kSVLGDOWSJCWsd+/ebL/99un9O+64g9LS0ro11rp1+fPhw1d9/Smn1O19JElSVhjKJUlqBA499FBatmwJQFFRES+99FLmjb711qqveffdzN9HkiTVmaFckqRGoEuXLvzhD39I7993333Mnz+/bo2ttVb588GDq7/ul78sf77eenV7L0mSlBFDuSRJjcSee+5Jt27dAJg3bx4PPfRQ3Rr67LPy5+++C61aVV6H/PPPoWVLGD++/FhRUd3eS5IkZcRQLklSI1FYWMjhhx+e3n/mmWf4+uuva99Q69ZQMdAvWQJrrw0hxI+ePaG4uPz8449nULUkScqEoVySpEZkq622ol+/fgCUlpbWfYm0/feHceNWfd2kSbDXXnV7D0mSlDFDuSRJjUgIgb/85S+EEAB47733mDBhQt0a22ILiCKYNQu6dIG8vPix+urxsSiC1A8AkiQpGYZySZIamfXXX5+ddtopvX/nnXeybNmyujfYuTPMnAklJfFjxoz4mCRJSpyhXJKkRuiggw6qtETaK6+8knBFkiSpPhjKJUlqhDp37sy+++6b3r/vvvtYvHhxghVJkqT6YCiXJKmR2nPPPemcGmb+008/8dhjj2X3DT7/HC6/HG68EWbPzm7bkiSpRgzlkiQ1Uq1ateKQQw5J7z/++OPMmDEj84aHDi1fGu3ss+GEE8ongjvxxMzblyRJNWYolySpEdthhx3YYIMNACguLub222+ve2Pz50OLFvDWW1WfX7YMbrgBOnas+3tIkqRaMZRLktSIhRA45phj0vtjx45l7NixdWusQ4d4GbS4YTjqqHh/yRLYcsvy6+bOha5dM6hakiTVlKFckqRGrlevXvz2t79N799+++21n/Rt6NDyQL7xxnGv+K23xvuFhTBmTHy+oCA+NmsWvPFGFqqXJEkrYyiXJCkHHHrooXTo0AGAGTNm8Mgjj9SugbIh6yHAp59Wf938+eXPhw2rZZWSJKm2DOWSJOWA9u3b8+c//zm9/8QTTzBt2rSavbjidUceufJrCwuhU6f4+YIFtStSkiTVmqFckqQcseOOO9KnTx8ASkpKGDVqFKWlpat+YcWl1MqGrK/M3nvXsUJJklRbhnJJknJECIHjjjuO/Px8AD777DOeeOKJVb+wXbvy58XFq75+7tw6VihJkmrLUC5JUg5Zd911+dOf/pTef+CBB/jqq69W/qI//rH8+dChq36Tp56qY3WSJKm2DOWSJOWYvffem549ewLxMPbrr79+5cPY27WL1ycHePfdlTf+8cewdGn8fJ11slCtJElaGUO5JEk5Ji8vj5NPPrnSMPbHKt43XpWTTy5/XlBQ9TD2jz+GTTct358wIfNiJUnSShnKJUnKQeussw4HHnhgev/BBx/ks88+q/4FV19dPqt6SQm0bBnvDx8O++4bz7peMZDvuit07lwfpUuSpAoM5ZIk5ai99tqLXr16AfEw9iuuuIIFK1vG7KefoFu38v05c+Cee+LZ2cuGrEMcyJ97rp6qliRJFRnKJUnKUXl5eZx22mm0adMGgB9++IEbb7yRKIqqf9EPP8Dbb1eekb3MuuvCrFkGckmSGpChXJKkHLbmmmty/PHHp/ffeustXnjhhZW/aKutYN48iKLKj6++csi6JEkNzFAuSVKOGzp0KLvuumt6/4477uB///tfghVJkqSaMpRLktQEHHHEEfTo0QOApUuXcumllzJnzpxki5IkSatkKJckqQkoLCzkzDPPpFWrVgDMmDGDSy+9lOKqlj6TJEmNhqFckqQmYu211+aMM84ghADAJ598wg033LDyid8kSVKiDOWSJDUhW2yxBYcffnh6///+7/94+OGHE6xIkiStjKFckqQm5ve//z277LJLev+BBx7gjTfeSLAiSZJUHUO5JElNTAiBo446iv79+6ePXXfddUyYMCG5oiRJUpVyPpSHEDqEEK4LIUwLISwOIUwNIZwXQiioZTtbhBD+HUL4MoSwKIRQFEJ4MoQwuL5qlySpvuTn53P22Wez9tprA1BSUsJll13G1KlTE65MkiRVlNOhPITQAXgL2A/4E7AacCZwFvBUCCGvhu3sB7wDbAz8EegM/A7oALwTQjgw+9VLklS/2rVrx4gRI+jatSsAixcv5qKLLqKoqCjZwiRJUlpOh3LgMqAvcGQURaOjKFoURdETwIXArsBRNWxnBPF/i8OjKBqbaucj4IDU+atD2VS2kiTlkK5duzJixAjat28PwLx58zjrrLP46KOPEq5MkiRBDofyEEJ74AjgO+D55U7fDUTAyTVsbr3U9uOKB6MomgHMBNYEutW1VkmSkrT22mtz8cUX07ZtWwAWLFjABRdcwLhx4xKuTJIk5WwoB3YAWgFjo+UWYI2iaBYwFdgohLBxDdr6ILXdtOLBEMIaQFdgKTA744olSUpIz549GTlyJJ06dQKguLiYSy+9lCeeeMJ1zCVJSlAuh/J+qW1RNefLjver5nxFxwDfAP8IIQwOIbQOIWwKPAgE4LYoipZmUKskSYlbf/31ufLKK1lzzTUBiKKIu+66i2uuuYYlS5YkXJ0kSc1TLofyNVPbn6o5/3Nqu8aqGoqiaAIwhLh3fSywEJgMbAicD5xU06JCCOOregC9a9qGJEn1pXv37lx55ZX06dMnfez//u//OOOMM5gxY0aClUmS1DzlcihvndpW14NdnNq2WVVDIYTtgPeJQ/jWQHtgc+BloB3QMqNKJUlqRFZbbTX+9re/scsuu6SPffnllxx//PG88sorDmeXJKkB5SddQAYWpbbVrUdemNouXFkjIYSOwCPEQXxgFEXfpk5NCCGcBHwJbB9C2DqKotJVFRVF0aBq3mc8MHBVr5ckqSHk5+dz7LHHsuGGG3LbbbdRUlLCwoULGTVqFGPGjOG4445L338uSZLqTy73lH+f2q5WzflOqe0Pq2hnGPHM6m9WCOQARFE0D3gOGAzsX7cyJUlqvHbZZRcuv/xyunfvnj42duxYjjnmGF566SWWLVuWYHWSJDV9uRzKP0xt16/mfI/lrqtO2XJo31Vzvuz4ZjWqSpKkHNOrVy9uuOEGdtttt/SxefPmceONN3L88cfz3nvv5eaQ9n32gRBWfBx5ZNKVSZKUlsuh/FVgCTA4hBAqngghdAE2Br6IomjqKtqZldp2r+b8L1JbZ1+XJDVZrVq14qijjmLEiBF07do1fXzatGlcfPHFnHfeeXz22WcJVlgLo0fH4fvxx6s+f8cd8fmiogYtS5KkquRsKE8NLb+TOEzvutzp4cRLmY0qOxBC6BBCeCaEcE8IIa/CtS8SB+5tQgiVgnkIoT1QNgvOK1n9AJIkNUKbbbYZt9xyCwceeCCtWrVKH580aRKnnHIKf/vb3/j6668TrHAVJkyAbbapfKxrV9hqK+jcufLx9deHn39uqMokSapSzobylHOAj4HbQwhDU+uL7wVcBLwE3Frh2p2B3wGHEM+sDkAURdOA84hnc/9PCGFICKFtCGEA8CSwOvBAFEWvNsDnkSQpca1ateKAAw7gjjvuYNiwYbRoUf7PhTFjxnDssccyatQovvrqqwSrrMbmm5c/HzgQogh+/BHefhtmzYr3169w51uXLg1foyRJFYScvEesgtTs6RcD+xBP2DYNuBe4Ioqi4grX/QJ4k3i4+nZRFC1arp1dgeOJJ3XrBMwHJgF3A/+MMvwPFUIYP3DgwIHjx4/PpBlJkhrc9OnTuf/++xk9evQK53r27MlOO+3EtttuS7t27RKoroJx42DIkPh5165xGK9OmzawKPVPgZ9+AmealyRlYNCgQbz//vvvV7ca18rkfCjPFYZySVKu++KLL7j33nt5//33VzhXUFDAVlttxU477cSAAQMq9a43mIpBe9EiqDD8fgXffw9lM86vKsBLkrQKhvIcYCiXJDUVkydP5rnnnmPMmDGUlJSscL5r165st912DBgwgD59+lS6N71etWgRD0+H8u3KlM0T26IFlJbWX12SpCYvk1CeXx8FSZKkpqtv37707duXefPm8cYbb/Dyyy/z+eefp8/PnDmTxx57jMcee4z8/Hx69uxJ//796d+/P71796awsLB+CisL4pUXZan56yRJSoA95Q3EnnJJUlNWVFTEyy+/zGuvvcbcuXOrva6goIBevXrRv39/+vXrx8Ybb5y9kJ6XB8uWxc9r01PesiUsXpydGiRJzZI95ZIkKVE9evTgiCOOYPjw4YwfP54JEyYwadIkpk2bVum6pUuXMnnyZCZPnpw+1qFDB7p27Vrto0uXLjUL7meeCSNHxs//+Ed48MHqr604S/v999fmo0qSlFX2lDcQe8olSc3RnDlz+PDDD5k0aRKTJk1i+vTpdWpnVcG9a9euFBQUVB66/txzsOuuKzZ2xx1w5JHl+/5bSJKUIXvKJUlSo9SxY0eGDh3K0KFDAZg9ezYffvghH374IZMnT+a7775jWdmQ85WYO3cuc+fO5csvv1zpe3Xt04euU6bQFWg7bBiFBQUUXnMNhRtsQOHUqRSccQaFJSUUAC2Bgr/+lcIvvqCwsJCCgoL0tmXLlhQUFBBqe3+6JEm1ZE95A7GnXJKkFS1btoyff/6ZH3/8kVmzZlW5nT17do2Ce9rbb8Ps2au+rnt3GLTyDo38/PxKgb3io6oQX5tzVe1XvD4vL6/mn1mSlCh7yiVJUk5q0aIFnTt3pnPnztVes2zZMn766Sdmzpy5wqPK4L711jB9OnzwQdUNhgBDhsTrk69CSUlJlcu+NYQWLVrU6AeB+vixID8/31ECktRADOWSJKlRa9GiBV26dKFLly706tWrymuqCu6LFy+meMECih97jOJp01i6zjoU77ADS5cupbi4uNKj7NjSpUtZsmRJej9Jy5YtY/HixSxOaGb4ikG9ZcuWrLnmmqy33nr06NGD9dZbj3XXXZeWLVsmUpskNSWGckmSlPNWGtwPO6xObUZRRElJSaXQvnyIr2p/Vecqhv7qfiBYsmQJSd9iWFbPggULAPj++++ZMGFC+nwIgTXXXDMd0su23bt3d+i9JNWCoVySJKkKIQQKCgriWd0TUFpaukKIz1boX9W5mgzZj6KI7777ju+++44xY8akjxcUFLDOOutUCuo9evSgc+fODomXpCoYyiVJkhqhvLw88vLyaNWqVYO/dxRFlYL6ggUL+Prrr/nqq6/46quvKCoq4ttvv62yN3/p0qV8+eWXK8yU36VLFzbbbDM233xzNttsMzp27NhQH0eSGjVnX28gzr4uSZKakuLiYr755huKioooKipKB/ZZs2bV6PUbbLBBOqBvsskmFBYW1nPFklR/nH1dkiRJDaqwsJANNtiADTbYoNLxefPm8dVXXzFt2rR0YC8qKmLRokWVrivrTX/ssccoLCxk0003ZfPNN2fo0KGsvvrqDflRJClR9pQ3EHvKJUlSc1VaWsrnn3/OBx98wAcffMCnn35KaWlpldeGEBgwYAA77rgjW221lTO8S8oJ9pRLkiSp0crLy6NXr1706tWLAw44gIULF/Lhhx8yYcIEPvjgA6ZPn56+NooiJkyYwIQJE2jTpg3bbLMNO+64I71793aiOElNkqFckiRJDapNmzYMGTKEIUOGADBjxgwmTJjA6NGjmTBhQnoCuYULF/Liiy/y4osvstZaa7Hjjjuyww470KVLlyTLl6Sscvh6A3H4uiRJ0qrNnDmTV199lVdeeYVvv/12hfMhBH75y1+y7777sskmmyRQoSStKJPh64byBmIolyRJqrkoivjkk094+eWXefPNN1eYKA6gT58+7LPPPgwePNih7ZISZSjPAYZySZKkulmyZAljxozh5ZdfZuLEiSucX2edddh///3ZdtttDeeSEmEozwGGckmSpMxNnz6dxx57jNdee42SkpJK53r37s1f//pXNtxww4Sqk9RcZRLKW9RHQZIkSVJ9WGuttTjhhBP4xz/+wV577UXr1q3T5z755BNOPvlk/v73vzN37twEq5SkmjOUS5IkKed06dKFww47jH/+85/su+++5OfHiwpFUcQLL7zAUUcdxSuvvIKjQiU1doZySZIk5ay2bdty6KGHctNNNzFoUPmo0fnz5zNq1ChGjBjBrFmzEqxQklbOUC5JkqSct9Zaa3HhhRdywQUXsMYaa6SPv/vuuxx77LH2mktqtAzlkiRJahJCCGyxxRbcdNNN7L777unjCxYsYNSoUdxwww0UFxcnWKEkrchQLkmSpCalVatWHHnkkYwcOZI111wzffzll1/mrLPOYubMmQlWJ0mVGcolSZLUJPXt25cbb7yRHXbYIX3ss88+46STTmLy5MkJViZJ5QzlkiRJarJatWrFSSedxJFHHkmLFvE/fefMmcO5557LK6+8knB1kmQolyRJUhMXQmD33Xfnsssuo2PHjgAsW7aMUaNG8dRTTyVcnaTmzlAuSZKkZqFv376MGjWK9ddfP33sH//4Bw899FCCVUlq7gzlkiRJaja6du3KyJEj6dOnT/rYAw88wNNPP51gVZKaM0O5JEmSmpW2bdtyySWXsPnmm6eP3XHHHYwePTrBqiQ1V4ZySZIkNTutWrXi3HPPpVevXgBEUcQ111zDhx9+mHBlkpobQ7kkSZKapZYtW3LBBRew1lprAVBSUsKll15KUVFRsoVJalYM5ZIkSWq2OnTowCWXXELnzp0BWLhwIRdeeCEzZsxIuDJJzYWhXJIkSc1at27duOiii2jTpg0As2fP5pJLLqG4uDjhyiQ1B4ZySZIkNXvrr78+5557Lvn5+QB89dVX3H///QlXJak5MJRLkiRJQP/+/fnLX/6S3n/yySf56KOPEqxIUnNgKJckSZJSdt111/RSaVEUMWrUKBYvXpxwVZKaMkO5JEmSlBJC4IQTTqBt27YAfP/999x5550JVyWpKTOUS5IkSRV07dqVo446Kr3/wgsvMH78+AQrktSUGcolSZKk5fz6179m6623Tu/ffPPNLF26NMGKJDVVhnJJkiRpOSEEjjnmGNq3bw/AjBkzeP755xOuSlJTZCiXJEmSqtCxY0f233//9P5DDz3EggULEqxIUlNkKJckSZKqMWzYMLp16wbAvHnzePzxxxOuSFJTYyiXJEmSqlFQUMDBBx+c3n/yySeZPXt2ghVJamoM5ZIkSdJKbLfddmywwQYAFBcX8+CDDyZckaSmxFAuSZIkrUQIgeHDh6f3X3rpJaZPn55cQZKaFEO5JEmStAqbbbYZAwYMAGDZsmU89thjCVckqakwlEuSJEmrEELgwAMPTO+PHTuWZcuWJViRpKbCUC5JkiTVQO/evencuTMAc+fOZcqUKQlXJKkpMJRLkiRJNRBCYPDgwen9sWPHJliNpKbCUC5JkiTV0JZbbpl+/s477xBFUYLVSGoKDOWSJElSDfXv359WrVoB8N133/HNN98kXJGkXGcolyRJkmqooKCAQYMGpfffeeedBKuR1BQYyiVJkqRaGDJkSPq595VLypShXJIkSaqFX/7yl7RoEf8z+tNPP2X27NkJVyQplxnKJUmSpFpo3749ffv2Te+PGzcuwWok5TpDuSRJklRLDmGXlC2GckmSJKmWKi6NNnHiRIqLixOsRlIuM5RLkiRJtdStWze6dOkCwNKlS/n555+TLUhSzjKUS5IkSXXQvn379PP58+cnWImkXGYolyRJkuqgbdu26ecLFy5MsBJJucxQLkmSJNVBmzZt0s8XLFiQYCWScpmhXJIkSaqDdu3apZ87fF1SXRnKJUmSpDqoOHzdnnJJdWUolyRJkuqgxqH81Vfhwgvhk08aoCpJucZQLkmSJNXBSkP5E09AixYQAuy4I1xyCfTpE++3bAnTpjVwtZIaK0O5JEmSVAfV3lPerx/svTdEUdUvLC6G9daDs86q5wol5QJDuSRJklQHVS6JttNOMHly+UUhwOOPxwH9uOMqN3DFFfDQQw1QqaTGzFAuSZIk1UGVS6K98kr5BXffDcuWwV57xfs33hiH82HDyq/54x/rv1BJjZqhXJIkSaqDij3l8+fPh9//vvzkAQfAoYdW/cJnn4VWrcr3vb9catYM5ZIkSVIdVLynfMGCBfD00+UnH3xw5S/++uvy55tskuXKJOUSQ7kkSZJUBxmtU961a/nzsvvRJTVLhnJJkiSpDpa/p7yaudYlaaUM5ZIkSVId5Ofn0yp1b3gURSyq7sJrromXSdtwQxg8GMaMgbffLj9fsddcUrOTn3QBkiRJUq5q27YtixcvBmDhaafR5uqr4xObbgobbBBP6lZxvfIvv4Stt67cSFFRwxQrqVGyp1ySJEmqo4pD2Ocfe2z5iY8/hmeeKQ/kLVpAQUF1jdRjhZIaO0O5JEmSVEcrzMA+cuSKF3XrBnvvDRUmhqvk9tvrqTpJucBQLkmSJNXRCjOwn3TSihfNmAGPPgo//1x1IyeeWC+1ScoNhnJJkiSpjlYI5XvsUX6yugnchgyJh7WfcEK8v3gxfPRRPVYpqTEzlEuSJEl1VDGUz58/H954I97p0AF+/DEO38s/3nknvub668sbqqqHXVKzYCiXJEmS6miFe8pLSuKdNdesWQMtUv8c//HHLFcmKVcYyiVJkqQ6qthTvnDhQsjLi3d++qlmDZTNzt6hQ5Yrk5QrDOWSJElSHVVaEm3+fOjZM9758cfyXvPq3HpreSg/9dR6qlBSY2colyRJkupoheHrzz9ffnLbbVf+4rKJ3vLyKk8QJ6lZMZRLkiRJdbTC7Otrrw2rrx4fGDMGBg1ascf800/jNcuXLo33jz66gaqV1BjlJ12AJEmSlKtWCOUA334LbdrEofv996GgADp1io/Nnh0vgVZmwAC48caGLVpSo2JPuSRJklRHVYby/HxYuLDyDOw//xyH9YqB/Pe/hwkTGqROSY2XoVySJEmqo4r3lM+fP7/8RH4+fPddPAv74MHxcPWWLeNZ1v/613iCt6eeSqBiSY2Nw9clSZKkOqo4+/rChQuJoogQQvkFnTrB2LENX5iknGFPuSRJklRHBQUFFBYWAlBaWsqSJUsSrkhSrjGUS5IkSRmo8r5ySaohQ7kkSZKUAUO5pEwYyiVJkqQMGMolZcJQLkmSJGWg2hnYJakGDOWSJElSBuwpl5QJQ7kkSZKUgYqhfOHChQlWIikXGcolSZKkDFQM5Q5fl1RbhnJJkiQpAw5fl5QJQ7kkSZKUAUO5pEwYyiVJkqQMGMolZSLnQ3kIoUMI4boQwrQQwuIQwtQQwnkhhII6tDUohPBgCGF6CGFJCOHbEMIrIYTj6qN2SZIk5T7vKZeUiZwO5SGEDsBbwH7An4DVgDOBs4CnQgh5tWjrcOBN4H1gENAJOBDoBRjKJUmSVKWK65TbUy6ptnI6lAOXAX2BI6MoGh1F0aIoip4ALgR2BY6qSSMhhEHA7cBZURRdFUXR96m2XgNOA76sp/olSZKU41wSTVImcjaUhxDaA0cA3wHPL3f6biACTq5hcyOA+cCty5+IouihKIqG1b1SSZIkNWVt2rRJP3f4uqTaytlQDuwAtALGRlEUVTwRRdEsYCqwUQhh45U1EkLoAuwMvBNFUXF9FStJkqSmafnh68v901SSViqXQ3m/1LaomvNlx/tVc77MFkAeMC2EMCyEMDqEsCCEMC+E8GYIYa/MS5UkSVJTVVBQQH5+PgAlJSUsXbo04Yok5ZJcDuVrprY/VXP+59R2jVW0s2Fq+xvgPuBaoDuwGTAPeDyEcGqdq5QkSVKTFkJwBnZJdZafdAEZaJ3aVvdTZNlQ9DbVnC/TIbVdDxgeRdHjqf25IYQDgG+Ay0MIj0ZR9NWqigohjK/mVO9VvVaSJEm5qV27dsyZMweIh7B37tw54Yok5Ypc7ilflNpWtx55YWpb0ykwI+CRSgeiaC7wNPGPF3vXtkBJkiQ1DxV7yl0WTVJt5HJP+fep7WrVnO+U2v6winbKhr/PjKJoURXny3rHe9akqCiKBlV1PNWDPrAmbUiSJCm3GMol1VUu95R/mNquX835HstdV50pqW11Pe5lnEZTkiRJVTKUS6qrXA7lrwJLgMEhhFDxRGqZs42BL6IomrqKdsYST+jWKYTQqYrz66W2n2RWriRJkpoqQ7mkusrZUB5F0TzgTuKZ0ndd7vRwIACjyg6EEDqEEJ4JIdwTQsir0M5i4B+p3YMqNhJCaA/sRnz/+r+z/BEkSZLURBjKJdVVzobylHOAj4HbQwhDQwitU+uKXwS8BNxa4dqdgd8BhwCbL9fOhcAEYEQI4fchhJYhhPWBB4G2wJFRFH2PJEmSVAVDuaS6yuWJ3oiiaE4IYWvgYuIA3Q2YBlwJXBFFUUmFy98GvgRmAR8t1868EMK2wLnAdcA6xEPa3wK2jaLo7fr+LJIkScpd7dq1Sz93nXJJtZHToRziYA6clHqs7LpvgQ1Xcn4ecFbqIUmSJNWYPeWS6irXh69LkiRJiasYyhcuXJhgJRV8/z2MGQOLFyddiaSVMJRLkiRJGaoYyhMdvv7RR7DBBhACdO8OW28NrVtDXh787ndQUrLqNiQ1KEO5JEmSlKFGMXz9pJOgb1/43/9WPLdsGTz3HBQWwqRJDV6apOoZyiVJkqQMVQzl8+bNa/gCLrsMrr++fH/TTeHrryGK4IEHoFOn+HgUwWabxUPbJTUKhnJJkiQpQx07diQ/P55Dec6cOcydO7dhCzj//HgbQhzGJ0+GtdeOj/3pT/DTT3DppfF+FMHgwQ1bn6RqGcolSZKkDOXn57PhhuUL/Xz66acN9+YXXxwHbYDXXy8P48s791zo3z9+/vXX3l8uNRKGckmSJCkLNt544/TzqVOnNtwbX311vC0ogG23Xfm1L75Y/vz44+uvJkk1ZiiXJEmSsqB3797p5w3aU75oUbxdf/1VX7vmmvEQd4AJE+qtJEk1ZyiXJEmSsmD5nvKobEh5fSsL2cuW1e51qXvgJSXLUC5JkiRlwRprrEGHDh2AeFm06dOnN8wbp96zyqXQljdpUvn959tvX381SaoxQ7kkSZKUBSEEevXqld5vsCHs11wTb0tL4c47V37tbruVP7/kkvqrSVKNGcolSZKkLKkYyhtssrfhwyEvL35+xBHwwgtVX7f33vGs61A+C7ukxBnKJUmSpCypGMo/+eSThnvjxx4rf77rrtCxI1xwATz0EOy7b3z/+BNPxOfz82H8+IarTdJKObuDJEmSlCU9e/ZMPy8qKmLJkiW0bNmy/t94jz3ggQfgwAPj/blzYcSIFa9r3RpmzHCSN6kRsadckiRJypK2bduyzjrrALBs2TK++OKLhnvzP/0Jli6FffYpH85epl07uOkmWLgwfi6p0fAnMkmSJCmLNt54Y75O3bv96aefsskmmzTcm+fnw6OPNtz7ScqYPeWSJElSFiUyA7uknGUolyRJkrLIUC6pNgzlkiRJUhatt9566cndZs6cyezZsxOuSFJjZiiXJEmSsigvL4+NNtoovW9vuaSVMZRLkiRJWVZxCPuUKVMSrERSY1fvoTyE0CmE8LsQwtYhhLDcubYhhAvquwZJkiSpIVWccf3VV19lyZIlCVYjqTGr11AeQtgUmAI8BYwG3g0hrFfhknbAhfVZgyRJktTQBg0axOqrrw7AnDlzePHFFxOuSFJjVd895SOBMUBHYC3gS+CtEELPen5fSZIkKTH5+fnsu+++6f3HHnuMpUuXJliRpMaqvkP5lsD5URQtiKLouyiK/gA8ArweQti4nt9bkiRJSsxOO+3EaqutBsDs2bN55ZVXEq5IUmNU36G8JRBVPBBF0SnEwfz/gD71/P6SJElSIgoLC9l7773T+//+978pKSlJsCJJjVFGoTyEcHoIoddKLvkU+OXyB6MoOpk4mD+VyftLkiRJjdkuu+xC+/btAZgxYwb/93//l3BFkhqbTHvKrwD2L9sJIWy83AzrTwB/rOqFURSdCNwPhKrOS5IkSbmuVatW7Lnnnun9hx9+mOLi4uQKktToZBrKlwL5FfanAOeW7URRNDKKol2re3EURcdGUeRa6ZIkSWqyfve739G2bVsAvvvuO+6///6EK5LUmGQaiL8FNq+wH7LQpiRJktRktG3bloMPPji9/8QTTzBp0qQEK5LUmGQaoJ8GfhdCeDaEcHjqWLSyF0iSJEnNzbBhwxg0aFB6/7rrrmP+/PkJViSpscg0lJ8LPAvsCtyeOnZeCGFCCOGuEMJxIYRfhRDaZvg+kiRJUs4KIXDCCSekJ32bOXMmt9xyC1Fkf5bU3GUUyqMomhdF0e7ES5sdnzr8M9ATGA7cALwBzAkhTAkh/CuEcFoIYYdM3leSJEnKNZ07d+a4445L77/xxhs8+uijCVYkqTHIyv3fURR9GkXRzandvwPtgf7EwfxG4G3gF8ABwJXAf7PxvpIkSVIu2Xrrrdlpp53S+/feey/PPfdcghVJSlr+qi+plX2Bb6MoWgZMTj3uLTsZQtgYGETlyeEkSZKkZuPoo49mxowZ6cnebr31Vtq1a8e2226bcGWSkpDVmdKjKHo8iqJ3VnJ+ahRFD0ZRdEY231eSJEnKFYWFhZx33nlsvPHGAERRxLXXXss771T7z2hJTZjLl0mSJEkNrHXr1lx00UWsu+66AJSWljJy5EheffXVhCuT1NAM5ZIkSVIC2rdvz4gRI+jevTsAy5Yt47rrruPpp59OuDJJDclQLkmSJCWkc+fOXHHFFfTo0SN97Pbbb+f222+npKQkucIkNRhDuSRJkpSg1VZbjZEjR9KnT5/0saeffppzzjmHWbNmJViZpIZgKJckSZIS1q5dOy655BK23HLL9LEpU6Zw4oknpmdpl9Q0GcolSZKkRqBVq1acc845DB8+nBACAHPmzOG8887jrrvuori4OOEKJdUHQ7kkSZLUSIQQ2Geffbjsssvo2LEjEC+Z9sQTT3DiiSfy6aefJlyhpGwzlEuSJEmNTL9+/bjhhhsYMGBA+tg333zD6aefzu233878+fMTrK6eHHII5OVBCOWP/Hw4+uikK5PqlaFckiRJaoQ6d+7MiBEjOOaYY2jVqhUQ95o//fTTHHnkkTz33HOUlpYmXGUWvPNOHMDvuw+WLat8rrQUbr0VWrSApEYJfPklnHkmrL8+tG0Lq60GO+wADz8M3lKgLAhRFCVdQ7MQQhg/cODAgePHj0+6FEmSJOWYGTNmcMMNNzBx4sRKx3v06MHw4cMZOHBg+j70nDJ5MvTrV76flwe77w7bbQf//S+88ELloP7tt5Ba173eLVsG550Hl18O1WWmHj3gqaegf/+GqUmN1qBBg3j//fffj6JoUG1fa0+5JEmS1Mh169aNESNGcPbZZ9OtW7f08aKiIi666CLOOOMMJk6cSM51uA0cWP781FOhpASeeAJOOgmefTbuKT/00PJrNtqo4Wo75RQYOTLupT/kEHj7bZg3D77/Hm6+GXr3hqKi+AeEKVMari41OfaUNxB7yiVJkpQNxcXFPPnkk/z73/9m8eLFlc717duXAw44gP79+zf+nvOKveTbbw+vvlr9tf36xdcD/PgjdO1av7W98grstBMUFsY94bvssuI1ixfDH/4ATz8Nv/wljBsXD8P//nv4059g/HhYuhRatoyHu99zD7RrV791KzGZ9JQbyhuIoVySJEnZNHv2bB599FGef/55SkpKKp3bYIMN2HPPPdlmm23Iz89PqMJVWG89mDYtfl5cDAUF1V+7cGF8PzdA377w4Yf1W9tee8GTT8LFF8MFF1R/3YIF8eeYNSvuSR8+HKZOrf76bbaBN97IdrVqBAzlOcBQLkmSpPrw448/8sgjj/Df//53hYnfunTpwu67784uu+xC27JQ21i0bh33NhcU1GzCtPz8eDh7hw4wZ0791fXjj7DmmvH97dOmxc9X5owz4Kqr4l71ss8RAmy6afzar76Czz4rv37DDeHzz+uvfiXCe8olSZKkZmr11Vfn2GOP5dZbb2XYsGEUFhamz82aNYu7776b4cOHc9ttt1FUVJRcocsrm8CttsPsl5+hPduKiuL36Nt31YEcYMcd421ZIN9nn3jY+ocfxpPVTZ0KS5bA4MHx+S++gP33r5fSlZsM5ZIkSVITsOaaa3L00Udz9913c9BBB9GpU6f0ucWLF/PMM89w/PHHc+qpp/Liiy+yaNGi5IqFuMcbar6sWNkogPq+n7xMTX8sqFj/b38Ljz4a97JXVFgIY8dCz57x/mOPZadGNQmGckmSJKkJad++Pfvvvz933nknJ5xwAuuss06l81OnTuWmm27ikEMO4YYbbuDTTz9NZtb2u+8uf37iiSu/dq+9yp8/8US9lJO27rpxIJ88GWbOXPX1Z55Z/vw//1n5tc88E29LS2HUqDqXqKbFe8obiPeUS5IkKQlRFDFp0iReeukl3n777RUmhQNYa621+PWvf822227LL37xi4YrrkWL8jXAn3sOdt11xWseeAAOOih+npcXL5tW33bbLV6SbeRIOOus6q9bvDiegG7ZsrgH/8cfV91227bxxHVbbw1vvZW9mpUo7ymXJEmSVKUQAgMGDOD000/nnnvu4Ygjjlih93z69Ok88MADHHXUUZx88sk88cQTzKxJL3Gm/vnP8ufDhsXB9p134v3//hc6dSoP5BAvP9YQjj023l58Mfzf/1V9zdKl8RrqZfe4d+5cs7Zbtoy3CxZkVqOaDHvKG4g95ZIkSWosoiji008/5cUXX2T06NErrHcOcZjfdNNN2W677dh6663pUHYPeLZddhmcd96qr7v5Zjj66PqpYXlRBEcdBXfcEd8Pfvjh8Ne/xpO/LVoU3xM+ahR88EE81D2Kaj4rfEFB3Nu/887w4ov1/lHUMFwSLQcYyiVJktQYLVmyhHfffZfXX3+d8ePHVzm8PYRAv3792Hrrrdlqq63oXNNe4Zr64gvYfnv4+usVz62/fjzMu3v37L7nqpSWwsknw403Vn/NmmvCgQfCNdfE+x9/DH36VH/9vffGvesA770Hg2qd39RIGcpzgKFckiRJjd2CBQt4++23eeONN5g4cWKVE8CFEOjduzdbb701W2+9Nd26dctuEV98ARMmwBZbxJOuJe2jj+CWW+Dhh+OJ3woKYLPN4p7zAw6ANm3Ke787dYqvWX72dYD586FLl3i29vpea10NzlCeAwzlkiRJyiU//fQTo0eP5s0332TKlCnVXrfhhhume9DXXnttQm3XHc8ly5bFw9WX/4xnnw2XXx4/b9MmHtr+l7+Unx8xAi65pHySuvvuq3yvvHKeoTwHGMolSZKUq2bPns0777zDW2+9xYcffljtEmprrbUWW221FVtttRU9e/Zs2gF9eQccEPeml2nRIu5BLy4un2Ee4vvnR4xo+PpUrwzlOcBQLkmSpKZg7ty5jB07lrfeeouJEydWeQ86QJcuXdIBfZNNNiE/P7+BK03ALbfEveZVDU3v1i2eOO73v2/4ulTvDOU5wFAuSZKkpmbBggW89957jBkzhvHjx1c5iztA27ZtGTRoEIMHD2bQoEG0a9eugSttYJMmwfXXw+zZ8QR1Z53VOO6PV70xlOcAQ7kkSZKasuLiYj744APGjBnDuHHjmDdvXpXXtWjRgk022YTBgwczePBg1lprrQauVMo+Q3kOMJRLkiSpuSgtLeWjjz5izJgxjB07lh9//LHaa9daa610QO/Tpw95Vc1cLjVyhvIcYCiXJElScxRFEUVFRYwbN45x48YxderUaq9t3749W2yxBUOGDGHgwIG0atWqASuV6s5QngMM5ZIkSVK81Np7773H2LFjmTBhAkuWLKnyuoKCAgYMGMCQIUMYPHgwnTt3buBKpZozlOcAQ7kkSZJUWXFxMZMmTWLs2LGMGzeO2bNnV3ttr169GDJkCFtuuWXTXw9dOcdQngMM5ZIkSVL1oiji888/55133mHcuHEUFRVVe+0vfvGLdEDv3bs3LVq0aLhCpSoYynOAoVySJEmque+//56xY8cyduxYPvroI5YtW1bldR07dmTw4MFsueWWbLbZZhQWFjZwpZKhPCcYyiVJkqS6mTdvHu+99x7vvPMO77//frXrobdu3Zott9ySoUOHsvnmm1NQUNDAlaq5MpTnAEO5JEmSlLni4mImTpyY7kX/+eefq7yubdu26YC+2WabkZ+f37CFqlkxlOcAQ7kkSZKUXVEU8emnnzJ27Fjeeustvvvuuyqva9u2LVtttVU6oLsWurLNUJ4DDOWSJElS/YmiiC+//JLRo0fz5ptv8sMPP1R5XefOndlhhx34zW9+wy9+8YsGrlJNlaE8BxjKJUmSpIZRNpP76NGjGT16NDNmzKjyur59+7LTTjvxq1/9ilatWjVwlWpKDOU5wFAuSZIkNbwoivjss8948803ef3116u8B71169Zst912/OY3v6Fnz56uga5aM5TnAEO5JEmSlKySkhLGjx/PSy+9xHvvvVflMmsbbLABu+yyC9tttx1t2rRJoErlIkN5DjCUS5IkSY3H7NmzefXVV3n55ZeZPn36CudbtWrFdtttxy677MJGG22UQIXKJYbyHGAolyRJkhqfKIqYMmUKL730Em+++SbFxcUrXNO7d2/22GMPttpqK2duV5UyCeUu1idJkiSp2QohsMkmm7DJJptwxBFH8Nprr/H888/z9ddfp6/55JNP+OSTT+jWrRu77bYbO++8M23btk2wajUl9pQ3EHvKJUmSpNxQ1nv+wgsv8Oabb1JSUlLpfKtWrdh5553Ze++96dKlS0JVqjHJpKe8RX0UJEmSJEm5qqz3/JRTTuHOO+9k//33p3379unzixcv5j//+Q+nnHLKCoFdqi1DuSRJkiRVo3Pnzhx00EHcfffdHHfccayzzjrpc7Nnz+bbb79NsDo1BYZySZIkSVqFwsJCfvvb3/L3v/+d9dZbL328qonhpNowlEuSJElSDYUQaN26dXp/6dKlCVajpsBQLkmSJEm1UFhYmH5uKFemDOWSJEmSVAsFBQXp5w5fV6YM5ZIkSZJUCxV7yg3lypShXJIkSZJqoWJPucPXlSlDuSRJkiTVgj3lyiZDuSRJkiTVghO9KZsM5ZIkSZJUC070pmwylEuSJElSLTh8XdlkKJckSZKkWnCiN2WToVySJEmSasHh68omQ7kkSZIk1YITvSmbDOWSJEmSVAuGcmWToVySJEmSasHh68omQ7kkSZIk1YKzryubDOWSJEmSVAsOX1c2GcolSZIkqRYcvq5sMpRLkiRJUi3YU65sMpRLkiRJUi3YU65sMpRLkiRJUi040ZuyyVAuSZIkSbXg8HVlU86H8hBChxDCdSGEaSGExSGEqSGE80IIBat+dbVtbh5CKAkhRCGEHlksV5IkSVKOc/i6sik/6QIyEULoALwFrAYcAIwHdgHuA7YOIeweRVFpLdvMA/4B5GW5XEmSJElNgKFc2ZTrPeWXAX2BI6MoGh1F0aIoip4ALgR2BY6qQ5unAp2BH7JXpiRJkqSmwuHryqacDeUhhPbAEcB3wPPLnb4biICTa9nmhsSB/ihgceZVSpIkSWpqKvaUG8qVqZwN5cAOQCtgbBRFUcUTURTNAqYCG4UQNq5Fm7cBj0dR9FL2ypQkSZLUlCwfypeLI1Kt5HIo75faFlVzvux4v2rOVxJCOAwYQC171yVJkiQ1LyEEe8uVNbk80duaqe1P1Zz/ObVdY1UNhRC6AVcDJ0RRNDOTokII46s51TuTdiVJkiQ1HoWFhekwvnTp0kr3mUu1kcs95a1T2+p+liqbBrFNDdq6ERgXRdH9GVclSZIkqclzBnZlSy73lC9Kbatbj7zsp6qFK2skhLAb8DviWdwzFkXRoGreZzwwMBvvIUmSJClZzsCubMnlnvLvU9vVqjnfKbWtdmmz1AzutwDnR1FUlLXKJEmSJDVp9pQrW3I5lH+Y2q5fzfkey11XlUHA2sC1IYSo4gNYL3XN/1LHijItWJIkSVLTULGn3FCuTORyKH8VWAIMDiGEiidCCF2AjYEvoiiaWl0DURS9HkVRqOoBfJW6bP3UsR719DkkSZIk5RhnX1e25Gwoj6JoHnAn0B3YdbnTw4EAjCo7EELoEEJ4JoRwTwghr6HqlCRJktT02FOubMnZUJ5yDvAxcHsIYWgIoXUIYS/gIuAl4NYK1+5MPKHbIcDmDV2oJEmSpKbDe8qVLTkdyqMomgNsDTwKPEi8NvmVqcfuURSVVLj8beBL4F3go6raCyH8eiX3lA+vlw8hSZIkKec4+7qyJZeXRAPSwfyk1GNl130LbLiKa14nHvYuSZIkSdXynnJlS073lEuSJElSErynXNliKJckSZKkWnL4urLFUC5JkiRJteREb8oWQ7kkSZIk1ZLD15UthnJJkiRJqiUnelO2GMolSZIkqZbsKVe2GMolSZIkqZac6E3ZYiiXJEmSpFpyojdli6FckiRJkmrJUK5sMZRLkiRJUi05fF3ZYiiXJEmSpFpy9nVli6FckiRJkmrJ2deVLYZySZIkSaolh68rWwzlkiRJklRLTvSmbDGUS5IkSVItOXxd2WIolyRJkqRacvi6ssVQLkmSJEm15PB1ZYuhXJIkSZJqyZ5yZYuhXJIkSZJqyZ5yZYuhXJIkSZJqyVCubDGUS5IkSVItVQzlS5cuJYqiBKtRLjOUS5IkSVIt5eXlkZeXB0AURZSWliZckXKVoVySJEmS6mD53nKpLgzlkiRJklQHFWdg975y1ZWhXJIkSZLqwGXRlA2GckmSJEmqA2dgVzYYyiVJkiSpDhy+rmwwlEuSJElSHTh8XdlgKJckSZKkOnD4urLBUC5JkiRJdWBPubLBUC5JkiRJdWBPubLBUC5JkiRJdWAoVzYYyiVJkiSpDhy+rmwwlEuSJElSHdhTrmwwlEuSJElSHdhTrmwwlEuSJElSHRjKlQ2GckmSJEmqA4evKxsM5ZIkSZJUBxV7yg3lqitDuSRJkiTVgcPXlQ2GckmSJEmqA4evKxsM5ZIkSZJUBw5fVzYYyiVJkiSpDir2lDt8XXVlKJckSZKkOnD4urLBUC5JkiRJdeBEb8oGQ7kkSZIk1YH3lCsbDOWSJEmSVAfeU65sMJRLkiRJUh04fF3ZYCiXJEmSpDpwojdlg6FckiRJkurAe8qVDYZySZIkSaoDh68rGwzlkiRJklQHDl9XNhjKJUmSJKkOHL6ubDCUS5IkSVIduCSassFQLkmSJEl1kJeXRwgBgNLSUkpLSxOuSLnIUC5JkiRJdRBCsLdcGTOUS5IkSVIdOQO7MmUolyRJkqQ6crI3ZcpQLkmSJEl15PB1ZcpQLkmSJEl15PB1ZcpQLkmSJEl1VLGn3OHrqgtDuSRJkiTVkfeUK1OGckmSJEmqI4evK1OGckmSJEmqI4evK1OGckmSJEmqI4evK1OGckmSJEmqI5dEU6YM5ZIkSZJUR/aUK1OGckmSJEmqI3vKlSlDuSRJkiTVkT3lypShXJIkSZLqyNnXlSlDuSRJkiTVkeuUK1OGckmSJEmqI+8pV6YM5ZIkSZJUR95TrkwZyiVJkiSpjhy+rkwZyiVJkiSpjpzoTZkylEuSJElSHTl8XZkylEuSJElSHTl8XZkylEuSJElSHTl8XZkylEuSJElSHdlTrkwZyiVJkiSpjuwpV6YM5ZIkSZJUR4ZyZcpQLkmSJEl15PB1ZcpQLkmSJEl1ZChXpgzlkiRJklRHDl9XpgzlkiRJklRH9pQrU4ZySZIkSaoje8qVKUO5JEmSJNVRxVC+dOlSoihKsBrlIkO5JEmSJNVRCGGFYC7VhqFckiRJkjLgfeXKhKFckiRJkjLgfeXKhKFckiRJkjLg8HVlwlAuSZIkSRmoOHzdnnLVlqFckiRJkjLg8HVlwlAuSZIkSRlwojdlwlAuSZIkSRkwlCsThnJJkiRJyoDD15UJQ7kkSZIkZcCecmXCUC5JkiRJGbCnXJkwlEuSJElSBlwSTZkwlEuSJElSBhy+rkwYyiVJkiQpAw5fVyYM5ZIkSZKUAYevKxOGckmSJEnKQMWecoevq7YM5ZIkSZKUAYevKxM5H8pDCB1CCNeFEKaFEBaHEKaGEM4LIRSs+tXpNn4dQvhnCOGLEMKSEMK8EMK4EMIJIYT8+qxfkiRJUm5zojdlIqdDeQihA/AWsB/wJ2A14EzgLOCpEEJeDdo4CHgN6A8cCnQGBgATgOuB5wzmkiRJkqpjKFcmcjqUA5cBfYEjoygaHUXRoiiKngAuBHYFjqpBG62AYmCPVBsLoij6MoqiI4HRwG+AQ+qpfkmSJEk5bpXD16MIvvgC3noL3nsP5sxpwOrU2OVsKA8htAeOAL4Dnl/u9N1ABJxcg6Z+BB6OouibKs49m9ruVMcyJUmSJDVx1c6+XlwMd90FW2wBG20EQ4fGz7t3h8MPh4kTE6hWjU3OhnJgB+Je7rFRFEUVT0RRNAuYCmwUQth4ZY1EUfRUFEXV9YTPS21DpsVKkiRJapqqnH39p59gxx3j8D1+PKy2Gmy5JfTvD4sWxWF90CC4446EqlZjkcuhvF9qW1TN+bLj/ao5XxNlgf6NDNqQJEmS1ISt0FNeXAy//z2MHg1rrQX33APffgtjxsS941OnwlFHQWkpHHkkPPxwgtUrabk8gdmaqe1P1Zz/ObVdoy6Np2Zv3xf4FrinFq8bX82p3nWpQ5IkSVLjtsJEbw8+WB7I334b1l238gt69oRbb423p50GJ54Ie+0FFdpR85HLPeWtU9vqpjcsu5mjTR3bPxPoDvw5iqKFdWxDkiRJUhO3wkRvN98c74wYsWIgr+iUU2DTTeGHH+DJJ+u3SDVauRzKF6W21a1HXvYzU60DdQjh18D5wClRFL1Um9dGUTSoqgfwSW3rkCRJktT4VRq+PnMmjBsHHTvC/vuv/IUhwF//Gj9/5JF6rFCNWS6H8u9T29WqOd8ptf2hNo2GEAYATwAjoygaVafKJEmSJDUblYavz0vNFb3BBtCmBoN2+6WmwPqhVrFFTUguh/IPU9v1qznfY7nrVimE0B94Bbg+iqKL6lyZJEmSpGYjP798qq7iZcviJwsW1OzF8+fH21atslyVckUuh/JXgSXA4BBCpSXLQghdiGdO/yKKoqk1aaxCIP97xUAeQlgnhPCXrFUtSZIkqUmp1FPeujW0bx/PsP5hDfoHH3003m6+eT1Vp8YuZ0N5FEXzgDuJJ2PbdbnTw4nXFh9VdiCE0CGE8EwI4Z4QQl7Fi0MI/YgD+S1RFF24XFsbAudmt3pJkiRJTUWlUB5FcPDB8c7VV6/8hV9/DQ89FD8/8sh6qk6NXS4viQZwDvBr4PYQwgHAeGAX4CLgJeDWCtfuDPwu9fxG4D2AEEJf4l73lsDGIYSHlnuPbvVUuyRJkqQmYIXZ1487Du64A+69N763/PzzocVy/aHTpsGuu8LixbDHHrDRRg1ctRqLnA7lURTNCSFsDVwMPEgcoKcBVwJXRFFUUuHyt4EvgVnARxWO7wt0TT2vbnrEr7JZtyRJkqSmY/lQHvXuTfjnP+Me84sugvvug6OOggEDYOFCePzxeLb1JUtgk03grruSK16Jy+lQDnEwB05KPVZ23bfEQ9GXP34Rcc+6JEmSJNVaXl4eeXl5lJaWEkURpaWl5B94IHToAMceC198AWecUflFIcDee8M//gGrVbeglJqDnA/lkiRJkpS0goICSktLAVi0aBHt27eH3XeHYcPg2Wfh4YfjZc9atox7zP/yF1i/uoWkslIQlFQYODx2LAweXH/vpzozlEuSJElShtZcc02KiooAeO211/j9738fn8jLg9//Pn7Ut9mzoUuXqs8NGRJvL7wwHlKvRiNnZ1+XJEmSpMZi2LBh6edPPvkkJRV7qRvCuHHVB/KKLr4Yfvvb+q9HNWYolyRJkqQM7bjjjnTs2BGAH3/8kbfeeqthCyjrCS9z4YUQRfFj7NjK5156KQ7xahQM5ZIkSZKUocLCQnbbbbf0/uOPP04URQ3z5r/4ReX9KKo8RH3w4PjYGmuUH1s+xCsxhnJJkiRJyoJhw4ZRWFgIwJdffsnEiRMb5o2/+678+cp+CPj++/qvRbVmKJckSZKkLOjQoQO/+c1v0vuPP/54gtVU41e/Kn9+003J1aE0Q7kkSZIkZcmee+5JCAGADz74gP/9738N9+Y1meht9Ojy57fcUn+1qMYM5ZIkSZKUJWuuuSa/qtAb/eijjzbcm//886qvqdg7XrHXXIkxlEuSJElSFu29997p52+88QbjGmqm89LSVV9z/PHlz2+/vf5qUY0ZyiVJkiQpi3r27MnWW2+d3r/++uuZPXt2/b1hiwqxbr31qr/uqqvqrwbVmaFckiRJkrLsuOOOo0vqHu+5c+cyatSo+lsirWIP+bRp0K7ditcMHQpnnFG+f/DB9VOLas1QLkmSJElZ1r59e0455ZRKk7795z//qb83rHh/+IIFEELlx1tvlZ/Pz4d7762/WlQrhnJJkiRJqgf9+/evdH/53Xffzeeff14/bzZ6NAwbturr2raFpUvrpwbViaFckiRJkurJQQcdxEYbbQRASUkJI0aMYObMmfXzZs8+C1EEW2yx4rmOHeNz8+fXz3urzgzlkiRJklRP8vPzOf3002nbti0As2fP5pJLLmHRokX196bjxsUBvOKjJsulKRGGckmSJEmqR7/4xS8455xzyMvLA+B///sfI0eOpLi4OOHK1BgYyiVJkiSpnvXv35/jjjsuvf/BBx9w6aWXGsxlKJckSZKkhrDTTjtx4IEHpvc/+OADRowYYTBv5gzlkiRJktRADjjggErBfMKECYwYMYIlS5YkWJWSZCiXJEmSpAZkMFdFhnJJkiRJamAHHHAABx98cHp/4sSJXHzxxSxevDjBqpQEQ7kkSZIkJeAPf/gDhxxySHr/ww8/5Pzzz2fBggUJVqWGZiiXJEmSpITst99+HHbYYen9Tz75hPPOO4958+YlWJUakqFckiRJkhK01157cdRRR6X3P//8c8444wy+//77BKtSQzGUS5IkSVLCdtttN44//nhCCAB88803nHbaaXzyyScJV6b6ZiiXJEmSpEZg55135vTTT6egoACAOXPmcM455/DGG28kXJnqk6FckiRJkhqJbbbZhssuu4wOHToAsHTpUq666iruu+8+oihKuDrVB0O5JEmSJDUiffr04eqrr2bttddOH3vkkUe47LLLWLRoUYKVqT4YyiVJkiSpkenevTtXX301gwYNSh8bO3Ysp556KtOnT0+wMmWboVySJEmSGqG2bdtywQUXsNdee6WPff3115xyyimMHTs2wcqUTYZySZIkSWqkWrRowWGHHcYpp5xCYWEhAAsXLuTSSy/lgQce8D7zJsBQLkmSJEmN3Pbbb8+VV17J6quvnj720EMPcfHFFzNv3rwEK1OmDOWSJEmSlAM23HBDRo0axYABA9LHxo8fz0knncRnn32WYGXKhKFckiRJknJEhw4duPjii9l3333Tx2bMmMEZZ5zBCy+84HD2HGQolyRJkqQckpeXx6GHHsp5551H27ZtASgpKeHvf/87V155JQsWLEi4QtWGoVySJEmSctCQIUO47rrr6NGjR/rY6NGjHc6eYwzlkiRJkpSjunfvzjXXXMMuu+ySPvb9999z+umn88QTTzicPQcYyiVJkiQphxUWFnLsscdy5pln0qZNGwBKS0u56667GDFiBHPnzk24Qq2MoVySJEmSmoChQ4dyww03sPHGG6ePvfvuu5xwwglMnjw5wcq0MoZySZIkSWoi1lhjDa644gr22muv9LFZs2Zxzjnn8NBDD7Fs2bIEq1NVDOWSJEmS1ITk5+dz2GGHccEFF9C+fXsAoijigQce4Nxzz2XmzJn188a/+x106BA/dtqpft6jCTKUS5IkSVITtMUWW3DDDTfQt2/f9LHJkydz/PHHM2bMmOy8ycKF0LIlhADPPQfz5sWPV16JjxUUxNeoWoZySZIkSWqiunbtymWXXcaBBx5ICAGA+fPn87e//Y2bb76Z4uLiujc+bRq0bQsra6OkJL7mk0/q/j5NnKFckiRJkpqwFi1acMABB3D55Zez+uqrp48///zznHzyyUybNq1uDa+3XuX9M8+EKIofI0dWPtenT93eoxkwlEuSJElSM7DJJptwww038Ktf/Sp9bNq0aZx88sm8+OKLtVvTfPPNy5+3axcH8csvLz921lnxsc6dy4+tv34G1TddhnJJkiRJaibatWvHmWeeyXHHHUdhYSEAxcXF3HTTTVx55ZUsWLCgZg1NmFD+fN686q+bNav8eVFRrettDgzlkiRJktSMhBD47W9/y3XXXce6666bPj569GhOPPFEPv3005o3tsYaq75mo43qUGXzYSiXJEmSpGZo3XXX5brrrmOXXXZJH/vhhx8488wzefzxx2s2nP2KK1Z9TcWZ3utrObYcZiiXJEmSpGaqsLCQY489ljPPPJO2bdsCUFpayj//+U8uvvhi5syZs/IGXn551W9y773lz9u0yaDapslQLkmSJEnN3NChQ7n++uvp1atX+tj48eM58cQTmTJlSvUvvP/+VTd+2mnlzw3lKzCUS5IkSZJYY401uPzyy9l3333Tx2bNmsXZZ5/NU089VXk4e35++fOHHqq+0VdfjWdhB0itk67KDOWSJEmSJADy8/M59NBDueiii2jfvj0QD2f/xz/+wRVXXMHChQvjC8eOLX/RH/8I1167YmP33AM77li+/+yz9Vh57jKUS5IkSZIqGTRoEKNGjaJnz57pY2+99RannHIKRUVFMHAgbLJJ+QtOPTXuCS8oiB8hwPDh5efXWw923bXB6s8lhnJJkiRJ0gq6devGFVdcwbBhw9LHpk+fzqmnnsprr70GH30EW2xR+UUlJfGjot69XaN8JQzlkiRJkqQqFRQUcPTRR3PaaafRsmVLAIqLi7n22mu59dZbKXn77fie8fXWW/HF3bvH51Y2UZwM5ZIkSZKkldtuu+247rrrWHvttdPHnn32Wc4++2xmzZoV94RHUeXHt98mV3AOMZRLkiRJklZpnXXW4dprr+VXv/pV+tgnn3zCiSeeyIcffphgZbnNUC5JkiRJqpHWrVtz5plncthhh9GiRRwn58yZw3nnncdzzz2XcHW5yVAuSZIkSaqxEAJ77bUXl156KR07dgRg2bJl3HLLLdx6662UlpYmXGFuMZRLkiRJkmqtX79+XH/99ZWWTXv22We56KKLWLBgQYKV5RZDuSRJkiSpTrp06cLIkSMZOnRo+tiECRM47bTT+O677xKsLHcYyiVJkiRJddayZUvOOOMM/vjHP6aPffPNN5x22mlMcTm0VTKUS5IkSZIyEkLgT3/6E6effjoFBQUAzJ07l3POOYfXX3892eIaOUO5JEmSJCkrtt12W/72t7+lJ4ArKSnhmmuu4ZFHHiGKooSra5wM5ZIkSZKkrOnduzfXXHMN66yzTvrYfffdx1133WUwr4KhXJIkSZKUVWussQZXXXUVAwYMSB978sknueGGG1i2bFmClTU+hnJJkiRJUta1bduWCy+8kK222ip97OWXX+bqq6+mpKQkwcoaF0O5JEmSJKleFBQUcOaZZ7LTTjulj7355ptcccUVLF26NMHKGg9DuSRJkiSp3uTl5XHCCSew2267pY+98847XHbZZRQXFydYWeNgKJckSZIk1asQAkceeSR77713+tj48eO5+OKLWbx4cYKVJc9QLkmSJEmqdyEEhg8fzgEHHJA+NmnSJC688MJmHcwN5ZIkSZKkBhFC4MADD+SQQw5JH/v444+59NJLm+1QdkO5JEmSJKlB7bfffhx++OHp/YkTJ3L55Zc3y1nZDeWSJEmSpAa35557cvDBB6f33333Xa655hpKS0sTrKrhGcolSZIkSYn4wx/+wH777ZfeHz16NHfeeWeCFTU8Q7kkSZIkKTEHH3wwu+++e3r/6aef5tlnn02wooZlKJckSZIkJSaEwF/+8heGDh2aPnb77bfz/vvvJ1hVwzGUS5IkSZISFULgpJNOomfPngAsW7aMK664gq+//jrhyuqfoVySJEmSlLiWLVty3nnn0bVrVwAWLlzIxRdfzJw5cxKurH4ZyiVJkiRJjULnzp05//zzadWqFQA//PADo0aNIoqihCurP4ZySZIkSVKjscEGG3Daaael99977z2ef/75BCuqX4ZySZIkSVKjMmTIEPbYY4/0/p133sk333yTYEX1x1AuSZIkSWp0DjnkENZbbz0AiouLufrqqykpKUm4quwzlEuSJEmSGp3CwkJOO+008vPzAfjiiy948MEHE64q+wzlkiRJkqRGqUePHhx66KEAdOrUiU022SThirIvP+kCJEmSJEmqzh577MGiRYsYNmwYHTt2TLqcrDOUS5IkSZIarRACf/zjH5Muo944fF2SJEmSpIQYyiVJkiRJSoihXJIkSZKkhBjKJUmSJElKiKFckiRJkqSEGMolSZIkSUqIoVySJEmSpIQYyiVJkiRJSoihXJIkSZKkhBjKJUmSJElKiKFckiRJkqSEGMolSZIkSUqIoVySJEmSpIQYyiVJkiRJSoihXJIkSZKkhOR8KA8hdAghXBdCmBZCWBxCmBpCOC+EUFDLdgpDCBeGED5LtfNVCOHqEEK7+qpdkiRJktS85SddQCZCCB2At4DVgAOA8cAuwH3A1iGE3aMoKq1BOwXAc8AWwEHAy8Bg4GFghxDCNlEULaifTyFJkiRJaq5yvaf8MqAvcGQURaOjKFoURdETwIXArsBRNWznBGBH4Owoip5OtfN/wLHA5qn2JEmSJEnKqpwN5SGE9sARwHfA88udvhuIgJNr0E4ATgKWEvewV/QkMBs4OoTQKqOCJUmSJElaTs6GcmAHoBUwNoqiqOKJKIpmAVOBjUIIG6+inf7A2sBHURTNW66dUuBdoB2wbbYKlyRJkiQJcjuU90tti6o5X3a8XzXns92OJEmSJEm1kssTva2Z2v5UzfmfU9s1GqgdAEII46s51bsmr5ckSZIkNR+53FPeOrVdWs354tS2TQO1I0mSJElSreRyT/mi1La69cgLU9uFDdQOAFEUDarqeKoHfWBN2pAkSZIkNQ+53FP+fWq7WjXnO6W2PzRQO5IkSZIk1Uou95R/mNquX835HstdV9/trEqPKVOmMGhQlR3pkiRJkqQcNWXKFCjPjrUSlltNLGek1in/kXgd8bUqLosWQuiSOvdlFEUbraKdAHwNdAO6VFwWLYSQB8wgHsK+ehRFizOo939AB1ac5b1sArhP6tq2lCV+F9VY+F1UY+F3UY2F30U1Fn4Xq9cDmBtFUXWdvdXK2Z7yKIrmhRDuBI4BdgWeq3B6OBCAUWUHQggdgH8Bs4DDUmuQE0VRFEK4HrgSOBi4uUI7ewKdgWsyCeSp96nyf5yy2dqruxddaih+F9VY+F1UY+F3UY2F30U1Fn4X60cu31MOcA7wMXB7CGFoCKF1CGEv4CLgJeDWCtfuDPwOOATYfLl2RgGvAyNDCLun2tkOuAmYmGpPkiRJkqSsytmecoAoiuaEELYGLgYeJB6CPo241/uKKIpKKlz+NvAlcU/5R8u1szSEsAtxyB8FrE08sduDwIVRFM2v548iSZIkSWqGcjqUQxzMgZNSj5Vd9y2w4UrOLwEuTD0kSZIkSap3uT58XZIkSZKknGUolyRJkiQpITm7JJokSZIkSbnOnnJJkiRJkhJiKJckSZIkKSGGckmSJEmSEmIolyRJkiQpIYZySZIkSZISYiiXJEmSJCkhhnJJkiRJkhJiKM+yEEKHEMJ1IYRpIYTFIYSpIYTzQggFtWynMIRwYQjhs1Q7X4UQrg4htKuv2tW0ZOO7GEL4dQjhnyGEL0IIS0II80II40IIJ4QQ8uuzfjUd2fpzcbk2Nw8hlIQQohBCjyyWqyYsm9/FEMKgEMKDIYTpqT8fvw0hvBJCOK4+alfTksV/L24RQvh3COHLEMKiEEJRCOHJEMLg+qpdTU8IoWsI4eHU36nD69iG2SUDhvIsCiF0AN4C9gP+BKwGnAmcBTwVQsirYTsFwHPAKanHasAhwEHAGyGEttmvXk1JNr6LIYSDgNeA/sChQGdgADABuB54zmCuVcnWn4vLtZkH/AOo9WvVfGXzuxhCOBx4E3gfGAR0Ag4EegGGcq1UFv+9uB/wDrAx8Efiv6d/B3QA3gkhHJj96tXUhBD2AT4Cds6gDbNLpqIo8pGlB3AjEAHDljt+aur4MTVsp8rrgX1Sx69M+rP6aNyPbHwXgSOAJcDaVZx7M9XOYUl/Vh+N+5GtPxeXe+0ZwP+A71Nt9Ej6c/po/I8s/h09CCgFTqji3AHAc0l/Vh+N+5HF7+Inqet/udzxbsAy4DsgJP15fTTeB3A08C3xjzl3p75Pw+vQjtklw0dI/QdThkII7YEZwE/AWlGF/7AhhC7Aj8AXURT1XEU7AZgGrAF0iaJoXoVzean3KARWj6JocdY/iHJeFr+LewD7RFF0SBXnzgJGAg9GUfSnbNavpiNb38Xl2twQmATsBdwOrAesH0VRURZLVxOTze9iCOE54FfEfw8X11PJaqKy/F1cBLQC2kZRtHC5czOA1YE1oyj6IYsfQU1ICGEo8FEURT+FEO4mHhn55yiK7q5FG2aXLHD4evbsQPwH49houV86oiiaBUwFNgohbLyKdvoDaxP/H2RexRNRFJUC7wLtgG2zVbianKx8F6MoeqqqQJ5S9t0MmRarJi1bfy5WdBvweBRFL2WvTDUDWfkupkLTzsA7BnLVUTb/XPwgtd204sEQwhpAV2ApMDvjitVkRVE0OoqinzJsxuySBYby7OmX2hZVc77seL9qzme7HTVfDfEdKvvHwhsZtKGmL6vfxRDCYcTzGpycUVVqjrL1XdyCeC6DaSGEYSGE0SGEBalJMN8MIeyVealq4rL55+IxwDfAP0IIg0MIrUMImwIPEv9oflsURUszqFWqCbNLFhjKs2fN1La6X5t+Tm3XaKB21HzV63coNZnHvsT3IN1TlzbUbGTtuxhC6AZcDZwcRdHMzEtTM5Ot7+KGqe1vgPuAa4HuwGbEI4geDyGcWucq1Rxk7c/FKIomAEOIe9fHAguBycTf0/OBk+peplRjZpcsMJRnT+vUtrpfJMuGubVpoHbUfNX3d+hM4n+E/nn5e9ik5WTzu3gjMC6KovszrkrNUba+ix1S2/WAU6IoejyKorlRFH1BPMnbPODyEMJ6GVWrpixrfy6GELYjXgFgQ2BroD2wOfAy8XDhlhlVKtWM2SULDOXZsyi1rW59ycLUdlUhJlvtqPmqt+9QCOHXxL++n+I9vaqBrHwXQwi7Ec8M+9cs1aXmJ9t/LkbAI5UORNFc4GkgH9i7tgWq2cjWn4sdib+DHYDdoigaE0XR/FTv+UnA4cBrdVl2Uqols0sWGMqz5/vUdrVqzndKbVc1A2a22lHzVS/foRDCAOAJYGQURaPqVJmam4y/i6mZim8BzneGdWUgW38ulg3PnBlF0aIqzn+V2tZ4RQE1O9n6Lg4jXvrszSiKvq14IjXZ1nPAYGD/upUp1ZjZJQsM5dnzYWq7fjXneyx3XX23o+Yr69+hEEJ/4BXg+iiKLqpzZWpusvFdHEQ8q+u1IYSo4oN4CDHA/1LHijItWE1Wtv5cnJLaVtcjVMb1ZlWdbH0Xy/78+66a82XHN6tRVVLdmV2ywFCePa8CS4DBqfX60lJLqGxMvO7k1FW0MwmYDmyS6iGq2E4e8cyv83HWa1UvW9/FsteUBfK/VwzkIYR1Qgh/yVrVaooy/i5GUfR6FEWhqgflvZLrp471qKfPodyXrT8XxxLfN94phNCpivNlQemTzMpVE5at7+Ks1LZ7Ned/kdo6+7rqm9klCwzlWZIaKnQn8R+Ouy53ejjx0hSjyg6EEDqEEJ4JIdxT8X6f1JqV1xP/Cn/wcu3sCXQmXuJicZY/gpqIbH0XU+f6EQfyW6IounC5tjYEzs1u9WpKsvldlDKRxb+jFwP/SO0eVLGR1D9GdyO+v/LfWf4IaiKy+Ofii8SBe5sQQqVgnvou7pLafSWrH0DNltmlnkVR5CNLD6Aj8BHxmpFDiWcj3Iv4V/UXgfwK1+5LPLwtAn65XDsFwGvAHGD3VDvbEQ9FmgC0S/qz+mjcj2x8F4G+wI/AXOChKh6vAkVJf1YfjfuRrT8Xq2m7KHVtj6Q/p4/G/8ji39HtgQ+I7y//PfEM1+sDzwAlwEFJf1YfjfuRxe/iGanj7xIvjdYWGEAcxCPg/qQ/q4/ceQB3p743w6s5b3apx0dI/YdUlqRmw7wY2Id4Ao5pwL3AFVEUFVe47hfAm8TDj7aLlpswJoTQEjiH+Jf4tYknR3gUuDCKf2WVVirT72II4SJg+d7x5X0VOWRYq5CtPxdT1/ya+C/+qvw5iqK7s1m7mpYs/h3dnnik0H7AOsRh6i3g8iiK3m6Aj6Icl8Xv4q7A8cSTunUiHiY8iThg/TPyH/paiRBCD+B/1Zyu9G88s0v9MpRLkiRJkpQQ7ymXJEmSJCkhhnJJkiRJkhJiKJckSZIkKSGGckmSJEmSEmIolyRJkiQpIYZySZIkSZISYiiXJEmSJCkhhnJJkiRJkhJiKJckSZIkKSGGckmSJEmSEmIolyRJkiQpIYZySZIkSZISYiiXJElZE0LoEEK4IYRQFEIoDiFEIYQzk65LkqTGKj/pAiRJUpPyALAb8BxwP1AC/CfRiiRJasRCFEVJ1yBJkpqAEEJvYArwYhRFuyRdjyRJucDh65IkKVt2SG0fS7QKSZJyiD3lkiQpIyGEfYBHqzndJ4qiTxqyHkmScon3lEuSpEx9B1wMHAOsBlyWOh4BnyVVlCRJucCeckmSlLEQQh4wD/g8iqL+SdcjSVKu8J5ySZKUDZsArYH3ky5EkqRcYiiXJEnZMDC1rRTKQwjbhhD+E0KYnlqzfHjDlyZJUuNlKJckSdlQFso/WO54O2AycCKwqEErkiQpBzjRmyRJyoaBxBO7Tah4MIqi54DnAEIIdzd4VZIkNXL2lEuSpIyEEAIwAPgsiqJ5/9/OHdsmFARRFJ0hhhaIkGNKcClugoLcBxK9UAAtjBNnRkLiCz9A52Srn7z0Svs3vQcAXokoBwCW+qiqTf29ug4A3CDKAYClrj7yBgDcJsoBgKVEOQDcSZQDAIvMzGFmemaO6S0A8Gq8vg4APEx3r6tq93tcVdW2u/dVdZmZc2wYADyJnpn0BgDgTXX3Z1Wdrnz6npmvfx0DAE9IlAMAAECIf8oBAAAgRJQDAABAiCgHAACAEFEOAAAAIaIcAAAAQkQ5AAAAhIhyAAAACBHlAAAAECLKAQAAIESUAwAAQIgoBwAAgBBRDgAAACGiHAAAAEJEOQAAAISIcgAAAAgR5QAAABDyA40pwK1wal85AAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": { "image/png": { "height": 372, "width": 498 }, "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "\n", "from pymoo.algorithms.moo.nsga3 import NSGA3\n", "from pymoo.factory import get_problem, get_reference_directions\n", "from pymoo.optimize import minimize\n", "from pymoo.problems.multi import TNK\n", "from pymoo.visualization.scatter import Scatter\n", "from pysamoo.algorithms.gpsaf import GPSAF\n", "\n", "problem = TNK()\n", "\n", "ref_dirs = get_reference_directions(\"das-dennis\", 2, n_points=20)\n", "\n", "# create the algorithm object\n", "algorithm = NSGA3(pop_size=20,\n", " n_offsprings=10,\n", " ref_dirs=ref_dirs)\n", "\n", "\n", "algorithm = GPSAF(algorithm,\n", " alpha=10,\n", " beta=50,\n", " n_max_doe=100,\n", " )\n", "\n", "res = minimize(\n", " problem,\n", " algorithm,\n", " ('n_evals', 200),\n", " seed=1,\n", " verbose=True)\n", "\n", "plot = Scatter()\n", "plot.add(problem.pareto_front(), plot_type=\"line\", color=\"black\", alpha=0.7)\n", "plot.add(res.F, facecolor=\"none\", edgecolor=\"red\")\n", "plot.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Tools" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Constrained Sampling for Design of Experiments (DOE)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For more information and more context, please see the following publication:" ] }, { "cell_type": "raw", "metadata": { "raw_mimetype": "text/restructuredtext", "tags": [] }, "source": [ "`Blank, J., Deb, K. (2021). Constrained Bi-objective Surrogate-Assisted Optimization of Problems with Heterogeneous Evaluation Times: Expensive Objectives and Inexpensive Constraints. In: , et al. Evolutionary Multi-Criterion Optimization. EMO 2021. Lecture Notes in Computer Science, vol 12654. Springer, Cham. `_\n", "\n", "::\n", "\n", " @InProceedings{10.1007/978-3-030-72062-9_21,\n", " author=\"Blank, Julian\n", " and Deb, Kalyanmoy\",\n", " editor=\"Ishibuchi, Hisao\n", " and Zhang, Qingfu\n", " and Cheng, Ran\n", " and Li, Ke\n", " and Li, Hui\n", " and Wang, Handing\n", " and Zhou, Aimin\",\n", " title=\"Constrained Bi-objective Surrogate-Assisted Optimization of Problems with Heterogeneous Evaluation Times: Expensive Objectives and Inexpensive Constraints\",\n", " booktitle=\"Evolutionary Multi-Criterion Optimization\",\n", " year=\"2021\",\n", " publisher=\"Springer International Publishing\",\n", " address=\"Cham\",\n", " pages=\"257--269\",\n", " isbn=\"978-3-030-72062-9\"\n", " }" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us say our goal is to find 50 feasible designs for the SRN problem. We assume that the constraints are computationally inexpensive in contrast to the objectives requiring a time-consuming evaluation." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "from pymoo.problems.multi import SRN\n", "problem = SRN()\n", "\n", "n_points = 50" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The function returning the constrained violation (CV) given a design can then be implemented by:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "def calc_cv(X):\n", " G = problem.evaluate(X, return_values_of=[\"G\"])\n", " return np.maximum(G, 0.0).sum(axis=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let us define a plot function showing the design space and show infeasible solutions in red and feasible ones in blue:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "\n", "def plot(X):\n", "\n", " xl, xu = problem.bounds()\n", "\n", " def circle(x=0, y=0, r=1):\n", " theta = np.linspace(0, 2 * np.pi, 100)\n", " return x + r * np.cos(theta), y + r * np.sin(theta)\n", "\n", "\n", " fig, ax = plt.subplots(figsize=(6, 6))\n", " \n", " feas = calc_cv(X) <= 0\n", "\n", " ax.scatter(X[feas, 0], X[feas, 1], s=30, facecolors='none', edgecolors='blue')\n", " ax.scatter(X[~feas, 0], X[~feas, 1], s=30, facecolors='none', edgecolors='red')\n", "\n", " x, y = circle(r=15)\n", " ax.plot(x, y, color=\"black\", alpha=0.6)\n", "\n", " x = np.linspace(-20, 20)\n", " y = 1 / 3 * x + 10 / 3\n", " ax.plot(x, y, color=\"black\", alpha=0.6)\n", "\n", " ax.set_aspect(1)\n", "\n", " ax.set_xlim(xl[0], xu[0])\n", " ax.set_ylim(xl[1], xu[1])\n", "\n", " ax.set_xlabel(\"$x_1$\")\n", " ax.set_ylabel(\"$x_2$\")\n", "\n", " plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we perform LHS sampling, the result might look as follows:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "image/png": { "height": 374, "width": 388 }, "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from pymoo.operators.sampling.lhs import LHS\n", "\n", "X = LHS().do(problem, n_points).get(\"X\")\n", "plot(X)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Clearly, the majority of designs are infeasible. A simple improvement of this method is repeating the sampling multiple times now until the required number of points has been found." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "image/png": { "height": 374, "width": 388 }, "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from pysamoo.sampling.rejection import RejectionConstrainedSampling\n", "\n", "X = RejectionConstrainedSampling(func_constr).do(problem, n_points).get(\"X\")\n", "plot(X)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By using a more sophisticated energy-based approach the uniformity can be further improved:" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "image/png": { "height": 374, "width": 388 }, "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from pysamoo.sampling.energy import EnergyConstrainedSampling\n", "\n", "X = EnergyConstrainedSampling(func_constr).do(problem, n_points).get(\"X\")\n", "plot(X)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Contact" ] }, { "cell_type": "raw", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ "\n", "Feel free to contact us if you have any question:\n", "\n", "::\n", "\n", " Julian Blank (blankjul [at] msu.edu)\n", " Michigan State University\n", " Computational Optimization and Innovation Laboratory (COIN)\n", " East Lansing, MI 48824, USA\n", "\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.8" }, "pycharm": { "stem_cell": { "cell_type": "raw", "metadata": { "collapsed": false }, "source": [] } } }, "nbformat": 4, "nbformat_minor": 4 }