{"title":"基于协作策略的量子粒子群优化算法","authors":"Di Zhou, Jun Sun, Wenbo Xu","doi":"10.1109/IWACI.2010.5585123","DOIUrl":null,"url":null,"abstract":"In this paper, Quantum-behaved Particle Swarm Optimization algorithm (QPSO) is investigated from the perspective of Estimation of Distribution Algorithms (EDAs) for the first time, which proves that QPSO is a combination of EDA and Standard Particle Swarm Optimization algorithm (SPSO). Additionally, a novel cooperative quantum-behaved particle swarm optimization algorithm (CQPSO) is proposed to prevent the Evolutionary Algorithms' universal tendency of premature convergence as a result of rapid decline in diversity. It is a type of parallel algorithm in which several QPSO algorithms are simulated individually in sub-swarms with frequent recombination which plays a roll of message passing. The most effective settings of Communication Frequency and the Size of Each Sub-Swarm for this novel algorithm are studied through experiments. Our experiments also show that CQPSO is able to find better solutions than the original QPSO and SPSO with higher efficiency.","PeriodicalId":189187,"journal":{"name":"Third International Workshop on Advanced Computational Intelligence","volume":"27 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"An advanced Quantum-behaved Particle Swarm Optimization algorithm utilizing cooperative strategy\",\"authors\":\"Di Zhou, Jun Sun, Wenbo Xu\",\"doi\":\"10.1109/IWACI.2010.5585123\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, Quantum-behaved Particle Swarm Optimization algorithm (QPSO) is investigated from the perspective of Estimation of Distribution Algorithms (EDAs) for the first time, which proves that QPSO is a combination of EDA and Standard Particle Swarm Optimization algorithm (SPSO). Additionally, a novel cooperative quantum-behaved particle swarm optimization algorithm (CQPSO) is proposed to prevent the Evolutionary Algorithms' universal tendency of premature convergence as a result of rapid decline in diversity. It is a type of parallel algorithm in which several QPSO algorithms are simulated individually in sub-swarms with frequent recombination which plays a roll of message passing. The most effective settings of Communication Frequency and the Size of Each Sub-Swarm for this novel algorithm are studied through experiments. Our experiments also show that CQPSO is able to find better solutions than the original QPSO and SPSO with higher efficiency.\",\"PeriodicalId\":189187,\"journal\":{\"name\":\"Third International Workshop on Advanced Computational Intelligence\",\"volume\":\"27 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-09-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Third International Workshop on Advanced Computational Intelligence\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IWACI.2010.5585123\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Third International Workshop on Advanced Computational Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IWACI.2010.5585123","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An advanced Quantum-behaved Particle Swarm Optimization algorithm utilizing cooperative strategy
In this paper, Quantum-behaved Particle Swarm Optimization algorithm (QPSO) is investigated from the perspective of Estimation of Distribution Algorithms (EDAs) for the first time, which proves that QPSO is a combination of EDA and Standard Particle Swarm Optimization algorithm (SPSO). Additionally, a novel cooperative quantum-behaved particle swarm optimization algorithm (CQPSO) is proposed to prevent the Evolutionary Algorithms' universal tendency of premature convergence as a result of rapid decline in diversity. It is a type of parallel algorithm in which several QPSO algorithms are simulated individually in sub-swarms with frequent recombination which plays a roll of message passing. The most effective settings of Communication Frequency and the Size of Each Sub-Swarm for this novel algorithm are studied through experiments. Our experiments also show that CQPSO is able to find better solutions than the original QPSO and SPSO with higher efficiency.