A Novel Cooperative Parallel Multi-Population Optimization Algorithm

N. Verma, Pooya Moradian Zadeh, Ziad Kobti
{"title":"A Novel Cooperative Parallel Multi-Population Optimization Algorithm","authors":"N. Verma, Pooya Moradian Zadeh, Ziad Kobti","doi":"10.1145/3571697.3571711","DOIUrl":null,"url":null,"abstract":"This work proposes a new parallel meta-heuristic optimization algorithm to deal with high dimensional optimization problems. We introduce a parallel and co-evolving multi population framework that mimics the hierarchical structure of grey wolves. We also propose using elite groups and a probabilistic mutation operator to improve the convergence speed and exploration ability. The algorithm is benchmarked on the twenty-eight functions of IEEE Congress of Evolutionary Computation (CEC) 2013 test suites and is compared with other meta-heuristic algorithms. Our proposed algorithm results show that our algorithm can find more optimal solutions at higher dimensions as compared to other meta-heuristic algorithms. Non-parametric statistical test also show the consistency in the obtained results.","PeriodicalId":400139,"journal":{"name":"Proceedings of the 2022 European Symposium on Software Engineering","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2022 European Symposium on Software Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3571697.3571711","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

This work proposes a new parallel meta-heuristic optimization algorithm to deal with high dimensional optimization problems. We introduce a parallel and co-evolving multi population framework that mimics the hierarchical structure of grey wolves. We also propose using elite groups and a probabilistic mutation operator to improve the convergence speed and exploration ability. The algorithm is benchmarked on the twenty-eight functions of IEEE Congress of Evolutionary Computation (CEC) 2013 test suites and is compared with other meta-heuristic algorithms. Our proposed algorithm results show that our algorithm can find more optimal solutions at higher dimensions as compared to other meta-heuristic algorithms. Non-parametric statistical test also show the consistency in the obtained results.
一种新的协同并行多种群优化算法
本文提出了一种新的并行元启发式优化算法来处理高维优化问题。我们引入了一个平行和共同进化的多种群框架,模仿灰狼的等级结构。我们还提出了使用精英群和概率变异算子来提高收敛速度和搜索能力。该算法以IEEE进化计算大会(CEC) 2013测试套件的28个功能为基准,并与其他元启发式算法进行了比较。我们提出的算法结果表明,与其他元启发式算法相比,我们的算法可以在更高的维度上找到更多的最优解。非参数统计检验也显示了所得结果的一致性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
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:604180095
Book学术官方微信