{"title":"一种改进运动规则的萤火虫群优化算法","authors":"Lifang He, Xiong Tong, Songwei Huang","doi":"10.1109/ICINIS.2012.16","DOIUrl":null,"url":null,"abstract":"At present, Glowworm Swarm Optimization (GSO) algorithm is a popular swarm intelligent optimization algorithm that has been used in many fields. However, basic GSO algorithm is easy to fall into local optimum, and has low accuracy and low speed of convergence in the later period. Thus, a new GSO algorithm is presented in this paper, in which Tent map of chaos is applied for the deployment of glowworms and a new movement rule is proposed. The simulation results prove that the effectiveness of the new GSO algorithm in the capture of the global optimum of several test functions, and the speed of convergence and accuracy are improved, compared with basic GSO algorithm.","PeriodicalId":302503,"journal":{"name":"2012 Fifth International Conference on Intelligent Networks and Intelligent Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"A Glowworm Swarm Optimization Algorithm with Improved Movement Rule\",\"authors\":\"Lifang He, Xiong Tong, Songwei Huang\",\"doi\":\"10.1109/ICINIS.2012.16\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"At present, Glowworm Swarm Optimization (GSO) algorithm is a popular swarm intelligent optimization algorithm that has been used in many fields. However, basic GSO algorithm is easy to fall into local optimum, and has low accuracy and low speed of convergence in the later period. Thus, a new GSO algorithm is presented in this paper, in which Tent map of chaos is applied for the deployment of glowworms and a new movement rule is proposed. The simulation results prove that the effectiveness of the new GSO algorithm in the capture of the global optimum of several test functions, and the speed of convergence and accuracy are improved, compared with basic GSO algorithm.\",\"PeriodicalId\":302503,\"journal\":{\"name\":\"2012 Fifth International Conference on Intelligent Networks and Intelligent Systems\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 Fifth International Conference on Intelligent Networks and Intelligent Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICINIS.2012.16\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 Fifth International Conference on Intelligent Networks and Intelligent Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICINIS.2012.16","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Glowworm Swarm Optimization Algorithm with Improved Movement Rule
At present, Glowworm Swarm Optimization (GSO) algorithm is a popular swarm intelligent optimization algorithm that has been used in many fields. However, basic GSO algorithm is easy to fall into local optimum, and has low accuracy and low speed of convergence in the later period. Thus, a new GSO algorithm is presented in this paper, in which Tent map of chaos is applied for the deployment of glowworms and a new movement rule is proposed. The simulation results prove that the effectiveness of the new GSO algorithm in the capture of the global optimum of several test functions, and the speed of convergence and accuracy are improved, compared with basic GSO algorithm.
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