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-rw-r--r-- | nlpsolver/ThirdParty/EvolutionarySolver/src/net/adaptivebox/deps/behavior/PSGTBehavior.java | 132 |
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diff --git a/nlpsolver/ThirdParty/EvolutionarySolver/src/net/adaptivebox/deps/behavior/PSGTBehavior.java b/nlpsolver/ThirdParty/EvolutionarySolver/src/net/adaptivebox/deps/behavior/PSGTBehavior.java new file mode 100644 index 000000000..68bf5a10e --- /dev/null +++ b/nlpsolver/ThirdParty/EvolutionarySolver/src/net/adaptivebox/deps/behavior/PSGTBehavior.java @@ -0,0 +1,132 @@ +/** + * Description: The description of particle swarm (PS) Generate-and-test Behavior. + * + #Supported parameters: + NAME VALUE_type Range DefaultV Description + c1 real [0, 2] 1.494 PSAgent: learning factor for pbest + c2 real [0, 2] 1.494 PSAgent: learning factor for gbest + w real [0, 1] 0.729 PSAgent: inertia weight + CL real [0, 0.1] 0 PSAgent: chaos factor + //Other choices for c1, c2, w, and CL: (2, 2, 0.4, 0.001) + + * Author Create/Modi Note + * Xiaofeng Xie May 11, 2004 + * Xiaofeng Xie Jul 01, 2008 + * + * This library is free software; you can redistribute it and/or + * modify it under the terms of the GNU Lesser General Public + * License as published by the Free Software Foundation; either + * version 2.1 of the License, or (at your option) any later version. + * + * This library is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU + * Lesser General Public License for more details. + * + * Please acknowledge the author(s) if you use this code in any way. + * + * @version 1.0 + * @Since MAOS1.0 + * + * References: + * [1] Kennedy J, Eberhart R C. Particle swarm optimization. IEEE Int. Conf. on + * Neural Networks, Perth, Australia, 1995: 1942-1948 + * For original particle swarm idea + * [2] Shi Y H, Eberhart R C. A Modified Particle Swarm Optimizer. IEEE Inter. Conf. + * on Evolutionary Computation, Anchorage, Alaska, 1998: 69-73 + * For the inertia weight: adjust the trade-off between exploitation & exploration + * [3] Clerc M, Kennedy J. The particle swarm - explosion, stability, and + * convergence in a multidimensional complex space. IEEE Trans. on Evolutionary + * Computation. 2002, 6 (1): 58-73 + * Constriction factor: ensures the convergence + * [4] Xie X F, Zhang W J, Yang Z L. A dissipative particle swarm optimization. + * Congress on Evolutionary Computation, Hawaii, USA, 2002: 1456-1461 + * The CL parameter + * [5] Xie X F, Zhang W J, Bi D C. Optimizing semiconductor devices by self- + * organizing particle swarm. Congress on Evolutionary Computation, Oregon, USA, + * 2004: 2017-2022 + * Further experimental analysis on the convergence of PSO + * [6] X F Xie, W J Zhang. SWAF: swarm algorithm framework for numerical + * optimization. Genetic and Evolutionary Computation Conference (GECCO), + * Seattle, WA, USA, 2004: 238-250 + * -> a generate-and-test behavior + * + */ + +package net.adaptivebox.deps.behavior; + +import net.adaptivebox.global.RandomGenerator; +import net.adaptivebox.goodness.IGoodnessCompareEngine; +import net.adaptivebox.knowledge.Library; +import net.adaptivebox.knowledge.SearchPoint; +import net.adaptivebox.problem.ProblemEncoder; +import net.adaptivebox.space.BasicPoint; +import net.adaptivebox.space.DesignSpace; + +public class PSGTBehavior extends AbsGTBehavior { + // Two normally choices for (c1, c2, weight), i.e., (2, 2, 0.4), or (1.494, + // 1.494, 0.729) The first is used in dissipative PSO (cf. [4]) as CL>0, and + // the second is achieved by using constriction factors (cf. [3]) + public double c1 = 2; + public double c2 = 2; + + //inertia weight + public double weight = 0.4; + + //See ref[4], normally be 0.001~0.005 + public double CL = 0; + + // the own memory: store the point that generated in old learning cycle + private BasicPoint pold_t; + + // the own memory: store the point that generated in last learning cycle + private BasicPoint pcurrent_t; + + @Override + public void setMemPoints(SearchPoint pbest, BasicPoint pcurrent, BasicPoint pold) { + pcurrent_t = pcurrent; + pbest_t = pbest; + pold_t = pold; + } + + @Override + public void generateBehavior(SearchPoint trailPoint, ProblemEncoder problemEncoder) { + DesignSpace designSpace = problemEncoder.getDesignSpace(); + + double[] pold_t_location = pold_t.getLocation(); + double[] pbest_t_location = pbest_t.getLocation(); + double[] pcurrent_t_location = pcurrent_t.getLocation(); + double[] gbest_t_location = socialLib.getGbest().getLocation(); + double[] trailPointLocation = trailPoint.getLocation(); + + int DIMENSION = designSpace.getDimension(); + for (int b = 0; b < DIMENSION; b++) { + if (RandomGenerator.doubleZeroOneRandom() < CL) { + designSpace.mutationAt(trailPointLocation, b); + continue; + } + + double deltaxb = weight * (pcurrent_t_location[b] - pold_t_location[b]) + + c1 * RandomGenerator.doubleZeroOneRandom() * (pbest_t_location[b] - pcurrent_t_location[b]) + + c2 * RandomGenerator.doubleZeroOneRandom() * (gbest_t_location[b] - pcurrent_t_location[b]); + + // limitation for delta_x + double deltaxbm = 0.5 * designSpace.getMagnitudeIn(b); + + if (deltaxb < -deltaxbm) { + deltaxb = -deltaxbm; + } else if (deltaxb > deltaxbm) { + deltaxb = deltaxbm; + } + + trailPointLocation[b] = pcurrent_t_location[b] + deltaxb; + } + } + + @Override + public void testBehavior(SearchPoint trailPoint, IGoodnessCompareEngine qualityComparator) { + Library.replace(qualityComparator, trailPoint, pbest_t); + pold_t.importLocation(pcurrent_t); + pcurrent_t.importLocation(trailPoint); + } +} |