基于粒子相互作用的粒子群优化收敛时间分析

Chao-Hong Chen, Ying-ping Chen
{"title":"基于粒子相互作用的粒子群优化收敛时间分析","authors":"Chao-Hong Chen, Ying-ping Chen","doi":"10.1155/2011/204750","DOIUrl":null,"url":null,"abstract":"We analyze the convergence time of particle swarm optimization (PSO) on the facet of particle interaction. We firstly introduce a statistical interpretation of social-only PSO in order to capture the essence of particle interaction, which is one of the key mechanisms of PSO. We then use the statistical model to obtain theoretical results on the convergence time. Since the theoretical analysis is conducted on the social-only model of PSO, instead of on common models in practice, to verify the validity of our results, numerical experiments are executed on benchmark functions with a regular PSO program.","PeriodicalId":7253,"journal":{"name":"Adv. Artif. Intell.","volume":"52 1","pages":"204750:1-204750:7"},"PeriodicalIF":0.0000,"publicationDate":"2011-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":"{\"title\":\"Convergence Time Analysis of Particle Swarm Optimization Based on Particle Interaction\",\"authors\":\"Chao-Hong Chen, Ying-ping Chen\",\"doi\":\"10.1155/2011/204750\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We analyze the convergence time of particle swarm optimization (PSO) on the facet of particle interaction. We firstly introduce a statistical interpretation of social-only PSO in order to capture the essence of particle interaction, which is one of the key mechanisms of PSO. We then use the statistical model to obtain theoretical results on the convergence time. Since the theoretical analysis is conducted on the social-only model of PSO, instead of on common models in practice, to verify the validity of our results, numerical experiments are executed on benchmark functions with a regular PSO program.\",\"PeriodicalId\":7253,\"journal\":{\"name\":\"Adv. Artif. Intell.\",\"volume\":\"52 1\",\"pages\":\"204750:1-204750:7\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"12\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Adv. Artif. Intell.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1155/2011/204750\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Adv. Artif. Intell.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1155/2011/204750","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 12

摘要

从粒子相互作用的角度分析了粒子群优化算法的收敛时间。为了捕捉粒子相互作用的本质,我们首先引入了纯社会粒子群的统计解释,粒子相互作用是粒子群的关键机制之一。然后利用统计模型得到了收敛时间的理论结果。由于理论分析是在PSO的社会模型上进行的,而不是在实践中常见的模型上进行的,为了验证结果的有效性,我们使用常规PSO程序对基准函数进行了数值实验。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Convergence Time Analysis of Particle Swarm Optimization Based on Particle Interaction
We analyze the convergence time of particle swarm optimization (PSO) on the facet of particle interaction. We firstly introduce a statistical interpretation of social-only PSO in order to capture the essence of particle interaction, which is one of the key mechanisms of PSO. We then use the statistical model to obtain theoretical results on the convergence time. Since the theoretical analysis is conducted on the social-only model of PSO, instead of on common models in practice, to verify the validity of our results, numerical experiments are executed on benchmark functions with a regular PSO program.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信