园林香脂优化算法的理论分析

IF 3.2 Q2 AUTOMATION & CONTROL SYSTEMS
Xiaohui Wang, Shengpu Li
{"title":"园林香脂优化算法的理论分析","authors":"Xiaohui Wang, Shengpu Li","doi":"10.1080/21642583.2022.2071778","DOIUrl":null,"url":null,"abstract":"Garden balsam optimization (GBO) is a new proposed evolutionary algorithm based on swarm intelligence. Convergence and time complexity analyses are very important in evolutionary computation, but the research on GBO is still blank. Same as other evolutionary algorithms, the optimization process of the GBO algorithm can be regarded as a Markov process. In this paper, a Markov stochastic model of the GBO algorithm is defined and used to prove the convergence of GBO algorithm. Finally, the approximation region of the estimated convergence time of GBO algorithm is calculated, which characterizes the evolution of the evolutionary process of the proposed algorithm.","PeriodicalId":46282,"journal":{"name":"Systems Science & Control Engineering","volume":null,"pages":null},"PeriodicalIF":3.2000,"publicationDate":"2022-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Theoretical analysis of garden balsam optimization algorithm\",\"authors\":\"Xiaohui Wang, Shengpu Li\",\"doi\":\"10.1080/21642583.2022.2071778\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Garden balsam optimization (GBO) is a new proposed evolutionary algorithm based on swarm intelligence. Convergence and time complexity analyses are very important in evolutionary computation, but the research on GBO is still blank. Same as other evolutionary algorithms, the optimization process of the GBO algorithm can be regarded as a Markov process. In this paper, a Markov stochastic model of the GBO algorithm is defined and used to prove the convergence of GBO algorithm. Finally, the approximation region of the estimated convergence time of GBO algorithm is calculated, which characterizes the evolution of the evolutionary process of the proposed algorithm.\",\"PeriodicalId\":46282,\"journal\":{\"name\":\"Systems Science & Control Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.2000,\"publicationDate\":\"2022-05-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Systems Science & Control Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/21642583.2022.2071778\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Systems Science & Control Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/21642583.2022.2071778","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0

摘要

Garden balsam optimization (GBO)是一种新的基于群体智能的进化算法。收敛性和时间复杂度分析在进化计算中非常重要,但对GBO的研究仍然是空白。与其他进化算法一样,GBO算法的优化过程可以看作是一个马尔可夫过程。本文定义了GBO算法的马尔可夫随机模型,并用该模型证明了GBO算法的收敛性。最后,计算了GBO算法估计收敛时间的近似区域,表征了该算法的演化过程。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Theoretical analysis of garden balsam optimization algorithm
Garden balsam optimization (GBO) is a new proposed evolutionary algorithm based on swarm intelligence. Convergence and time complexity analyses are very important in evolutionary computation, but the research on GBO is still blank. Same as other evolutionary algorithms, the optimization process of the GBO algorithm can be regarded as a Markov process. In this paper, a Markov stochastic model of the GBO algorithm is defined and used to prove the convergence of GBO algorithm. Finally, the approximation region of the estimated convergence time of GBO algorithm is calculated, which characterizes the evolution of the evolutionary process of the proposed algorithm.
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
Systems Science & Control Engineering
Systems Science & Control Engineering AUTOMATION & CONTROL SYSTEMS-
CiteScore
9.50
自引率
2.40%
发文量
70
审稿时长
29 weeks
期刊介绍: Systems Science & Control Engineering is a world-leading fully open access journal covering all areas of theoretical and applied systems science and control engineering. The journal encourages the submission of original articles, reviews and short communications in areas including, but not limited to: · artificial intelligence · complex systems · complex networks · control theory · control applications · cybernetics · dynamical systems theory · operations research · systems biology · systems dynamics · systems ecology · systems engineering · systems psychology · systems theory
×
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