{"title":"改进蚁群算法的应用","authors":"Hongyan Shi, Zhaoyu Bei","doi":"10.1109/ICNC.2008.75","DOIUrl":null,"url":null,"abstract":"A stochastic optimization algorithm is proposed by combining ant colony (ACO) algorithm with artificial fish-swarm algorithm (AFSA) for solving continuous space optimization problems. The algorithm is improved with the rapid search capability of AFSA and the good search characteristics of ACO, and the convergence speed of the presented algorithm is also improved for avoiding being trapped in local optimization. The improved algorithm has been tested for varieties of functions. And the algorithm can handle these optimization problems very well.","PeriodicalId":6404,"journal":{"name":"2008 Fourth International Conference on Natural Computation","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2008-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"59","resultStr":"{\"title\":\"Application of Improved Ant Colony Algorithm\",\"authors\":\"Hongyan Shi, Zhaoyu Bei\",\"doi\":\"10.1109/ICNC.2008.75\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A stochastic optimization algorithm is proposed by combining ant colony (ACO) algorithm with artificial fish-swarm algorithm (AFSA) for solving continuous space optimization problems. The algorithm is improved with the rapid search capability of AFSA and the good search characteristics of ACO, and the convergence speed of the presented algorithm is also improved for avoiding being trapped in local optimization. The improved algorithm has been tested for varieties of functions. And the algorithm can handle these optimization problems very well.\",\"PeriodicalId\":6404,\"journal\":{\"name\":\"2008 Fourth International Conference on Natural Computation\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-10-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"59\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 Fourth International Conference on Natural Computation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICNC.2008.75\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 Fourth International Conference on Natural Computation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICNC.2008.75","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A stochastic optimization algorithm is proposed by combining ant colony (ACO) algorithm with artificial fish-swarm algorithm (AFSA) for solving continuous space optimization problems. The algorithm is improved with the rapid search capability of AFSA and the good search characteristics of ACO, and the convergence speed of the presented algorithm is also improved for avoiding being trapped in local optimization. The improved algorithm has been tested for varieties of functions. And the algorithm can handle these optimization problems very well.
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