K. Brezinski, Michael Guevarra, K. Ferens
{"title":"基于种群平衡的混合粒子群算法组合优化研究","authors":"K. Brezinski, Michael Guevarra, K. Ferens","doi":"10.4018/ijssci.2020040105","DOIUrl":null,"url":null,"abstract":"Thisarticleintroducesahybridalgorithmcombiningsimulatedannealing(SA)andparticleswarm optimization (PSO) to improve the convergence time of a series of combinatorial optimization problems.TheimplementationcarriedoutadynamicdeterminationoftheequilibriumloopsinSA throughasimple,yeteffectivedeterminationbasedontherecentperformanceoftheswarmmembers. Inparticular,theauthorsdemonstratedthatstrongimprovementsinconvergencetimefollowedfrom amarginaldecreaseinglobalsearchefficiencycomparedtothatofSAalone,forseveralbenchmark instancesofthetravelingsalespersonproblem(TSP).Followingtestingon4additionalcitylistTSP problems,a30%decreaseinconvergencetimewasachieved.Allinall,thehybridimplementation minimizedtherelianceonparametertuningofSA,leadingtosignificantimprovementstoconvergence timecomparedtothoseobtainedwithSAaloneforthe15benchmarkproblemstested. KEywORdS Cognition, Combinatorial Optimization, Global Optimization, Metaheuristics, Particle Swarm Optimization, Simulated Annealing, Swarm Intelligence, Traveling Salesperson Problem","PeriodicalId":432255,"journal":{"name":"Int. J. Softw. Sci. Comput. Intell.","volume":"49 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":"{\"title\":\"Population Based Equilibrium in Hybrid SA/PSO for Combinatorial Optimization: Hybrid SA/PSO for Combinatorial Optimization\",\"authors\":\"K. Brezinski, Michael Guevarra, K. Ferens\",\"doi\":\"10.4018/ijssci.2020040105\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Thisarticleintroducesahybridalgorithmcombiningsimulatedannealing(SA)andparticleswarm optimization (PSO) to improve the convergence time of a series of combinatorial optimization problems.TheimplementationcarriedoutadynamicdeterminationoftheequilibriumloopsinSA throughasimple,yeteffectivedeterminationbasedontherecentperformanceoftheswarmmembers. Inparticular,theauthorsdemonstratedthatstrongimprovementsinconvergencetimefollowedfrom amarginaldecreaseinglobalsearchefficiencycomparedtothatofSAalone,forseveralbenchmark instancesofthetravelingsalespersonproblem(TSP).Followingtestingon4additionalcitylistTSP problems,a30%decreaseinconvergencetimewasachieved.Allinall,thehybridimplementation minimizedtherelianceonparametertuningofSA,leadingtosignificantimprovementstoconvergence timecomparedtothoseobtainedwithSAaloneforthe15benchmarkproblemstested. KEywORdS Cognition, Combinatorial Optimization, Global Optimization, Metaheuristics, Particle Swarm Optimization, Simulated Annealing, Swarm Intelligence, Traveling Salesperson Problem\",\"PeriodicalId\":432255,\"journal\":{\"name\":\"Int. J. Softw. Sci. Comput. Intell.\",\"volume\":\"49 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"12\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Int. J. Softw. Sci. Comput. Intell.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4018/ijssci.2020040105\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Int. J. Softw. Sci. Comput. Intell.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4018/ijssci.2020040105","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 12
Population Based Equilibrium in Hybrid SA/PSO for Combinatorial Optimization: Hybrid SA/PSO for Combinatorial Optimization
Thisarticleintroducesahybridalgorithmcombiningsimulatedannealing(SA)andparticleswarm optimization (PSO) to improve the convergence time of a series of combinatorial optimization problems.TheimplementationcarriedoutadynamicdeterminationoftheequilibriumloopsinSA throughasimple,yeteffectivedeterminationbasedontherecentperformanceoftheswarmmembers. Inparticular,theauthorsdemonstratedthatstrongimprovementsinconvergencetimefollowedfrom amarginaldecreaseinglobalsearchefficiencycomparedtothatofSAalone,forseveralbenchmark instancesofthetravelingsalespersonproblem(TSP).Followingtestingon4additionalcitylistTSP problems,a30%decreaseinconvergencetimewasachieved.Allinall,thehybridimplementation minimizedtherelianceonparametertuningofSA,leadingtosignificantimprovementstoconvergence timecomparedtothoseobtainedwithSAaloneforthe15benchmarkproblemstested. KEywORdS Cognition, Combinatorial Optimization, Global Optimization, Metaheuristics, Particle Swarm Optimization, Simulated Annealing, Swarm Intelligence, Traveling Salesperson Problem