文件列表:

deps\.DS_Store (6148, 2009-06-27)
deps\release\.DS_Store (6148, 2009-06-20)
deps\release\DEPSAgent.class (3984, 2009-06-20)
deps\release\DEPSGroup.class (3549, 2009-06-20)
deps\release\DEPSO.class (5894, 2009-06-20)
deps\release\encode\EvalElement.class (927, 2009-06-20)
deps\release\encode\EvalStruct.class (974, 2009-06-20)
deps\release\encode\IEncodeEngine.class (145, 2009-06-20)
deps\release\Global\BasicBound.class (769, 2009-06-20)
deps\release\Global\GlobalString.class (1893, 2009-06-20)
deps\release\Global\OutputMethods.class (1232, 2009-06-20)
deps\release\Global\RandomGenerator.class (2144, 2009-06-20)
deps\release\goodness\ACRComparator.class (1655, 2009-06-20)
deps\release\goodness\BCHComparator.class (615, 2009-06-20)
deps\release\goodness\IGoodnessCompareEngine.class (282, 2009-06-20)
deps\release\goodness\IUpdateCycleEngine.class (155, 2009-06-20)
deps\release\ICycleOutputEngine.class (146, 2009-06-20)
deps\release\knowledge\IStateSetEngine.class (201, 2009-06-20)
deps\release\knowledge\SearchPoint.class (1631, 2009-06-20)
deps\release\knowledge\SearchPointSet.class (1012, 2009-06-20)
deps\release\knowledge\StateInfoHandler.class (1886, 2009-06-20)
deps\release\problem\.DS_Store (6148, 2009-05-19)
deps\release\problem\constrained\.DS_Store (6148, 2009-04-30)
deps\release\problem\constrained\Michalewicz_G1.class (1331, 2009-06-20)
deps\release\problem\constrained\Michalewicz_G10.class (1224, 2009-06-20)
deps\release\problem\constrained\Michalewicz_G11.class (898, 2009-06-20)
deps\release\problem\constrained\Michalewicz_G12.class (1197, 2009-06-20)
deps\release\problem\constrained\Michalewicz_G13.class (1090, 2009-06-20)
deps\release\problem\constrained\Michalewicz_G2.class (1308, 2009-06-20)
deps\release\problem\constrained\Michalewicz_G3.class (1071, 2009-06-20)
deps\release\problem\constrained\Michalewicz_G4.class (1288, 2009-06-20)
deps\release\problem\constrained\Michalewicz_G5.class (1384, 2009-06-20)
deps\release\problem\constrained\Michalewicz_G6.class (1108, 2009-06-20)
deps\release\problem\constrained\Michalewicz_G7.class (1806, 2009-06-20)
deps\release\problem\constrained\Michalewicz_G8.class (1039, 2009-06-20)
deps\release\problem\constrained\Michalewicz_G9.class (1455, 2009-06-20)
deps\release\problem\constrained\PressureVessel.class (1301, 2009-06-20)
deps\release\problem\constrained\WeldedBeam.class (1650, 2009-06-20)
deps\release\problem\ProblemEncoder.class (2661, 2009-06-20)
... ...

#Software description: The DEPSO algorithm V1.0.001 for (constrained) numerical optimization #Command-line examples $ cd release $ java DEPSO [NAME=VALUE] ... //See release\RUNExample.bat for more examples. $ cd release $ sh RUNExample.bat #Problems to be solved For existing examples, please refers to the files located in the directories: problem/constrained and problem/unconstrained For creating new instances, please refers to the source code files: 1) problem/constrained/Michalewicz_G1.java, for constrained optimization; 2) problem/constrained/Michalewicz_G3.java, for equality constraints; 3) problem/unconstrained/GoldsteinPrice.java, for unconstrained optimization. Please see http://www.adaptivebox.net/doi/DEPSO for downloading the source code of the up-to-date list of implemented benchmark instances. #Setting parameters: NAME VALUE_type Range DefaultV Description Problem String * The problem to be solved //For example: problem.constrained.Michalewicz_G2 is the default value ---------------------------------------------------------------------------------- N integer >5 70 The number of agents T integer >1 2000 The maximum learning cycles isACR boolean false Constraint-handling: BCH(false), ACR(true) //Basic constraint-handling (BCH) rule or adaptive constraints relaxing (ACR) rule Tout integer >0 100 The output interval (not important) //The total evalution times is about N*T FACTOR real (0, 1.2] 0.5 DE: scale constant CR real [0, 1] 0.9 DE: crossover constant //The parameters of DE operator c1 real [0, 2] 1.494 PSO: learning factor for pbest c2 real [0, 2] 1.494 PSO: learning factor for gbest weight real [0, 1] 0.729 PSO: inertia weight //The parameters of PSO operator #Output Information: The program outputs information of the best solution every "Tout" cycles. At the end, it outputs the location and optimum values of the best point. //Vcon: the weighted constraint violation value (>=0): if Vcon==0, then no violation //Vopt: the value of objective function #Version History -> Version: V1.0.001 (http://www.adaptivebox.net/research/download/maos/depso/depsV1.0.001.zip) * For boundary-handling, the cycled version [3] instead of the periodic version [1] is considered, so that all new solutions are generated within the original search space, as well as the agents are searching within a virtually infinite space. * The adaptive constraints relaxing (ACR) rule [2] might tackle the problem with equality constraints more efficiently than the basic constraint-handling (BCH) rule does. * Newly introduced parameters: isACR -> Version: V1.0.000 (http://www.adaptivebox.net/research/download/maos/depso/depsV1.0.000.zip) * It implements the original DEPSO algorithm in [1]. * Setting parameters: Problem, N, T, Tout, FACTOR, CR, c1, c2, weight #Reference: [1] W.-J. Zhang, X.-F Xie. DEPSO: Hybrid particle swarm with differential evolution operator. IEEE International Conference on Systems, Man & Cybernetics, Washington, DC, USA, 2003: 3816-3821. -> For the original DEPSO algorithm [2] X.-F. Xie, W.-J. Zhang, D.-C. Bi. Handling equality constraints by adaptive relaxing rule for swarm algorithms. Congress on Evolutionary Computation (CEC), Portland, OR, USA, 2004: 2012-2016. -> For handling equality constraints: the adaptive constraint-relaxing rule (ACR) [3] X.-F. Xie, J. Liu. A compact multiagent system based on autonomy oriented computing, IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT), Compigne, France, 2005: 38-44. -> For boundary-handling: the cycled version for PSO, the random version for DE # License description: ******************************************************************* * DEPSO 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 3.0 of the License, or (at your option) any later version. * * DEPSO 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 3.0 for more details. * * Please acknowledge the author(s) if you use this code in any way. ******************************************************************* #Contact Information: Portal: http://www.adaptivebox.net/doi/DEPSO EMAIL: xiexiaofeng@tsinghua.org.cn

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