A Multi-objective Gravitational Search Algorithm

H. Hassanzadeh, M. Rouhani
{"title":"A Multi-objective Gravitational Search Algorithm","authors":"H. Hassanzadeh, M. Rouhani","doi":"10.1109/CICSYN.2010.32","DOIUrl":null,"url":null,"abstract":"Recently there has been a great research conducted on diverse variations of multi-objective swarm optimization algorithms each of which might have its own strengths and weaknesses. Due to the high complexity of multi-objective problems the efficiency of these methods has become a matter of concern. In this paper a new multi-objective meta-heuristic algorithm based on gravitational forces is proposed and applied to different test benches. The acquired results proved the superiority of the algorithm comparing with other pioneering techniques such as the MOPSO","PeriodicalId":358023,"journal":{"name":"2010 2nd International Conference on Computational Intelligence, Communication Systems and Networks","volume":"141 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"105","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 2nd International Conference on Computational Intelligence, Communication Systems and Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CICSYN.2010.32","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 105

Abstract

Recently there has been a great research conducted on diverse variations of multi-objective swarm optimization algorithms each of which might have its own strengths and weaknesses. Due to the high complexity of multi-objective problems the efficiency of these methods has become a matter of concern. In this paper a new multi-objective meta-heuristic algorithm based on gravitational forces is proposed and applied to different test benches. The acquired results proved the superiority of the algorithm comparing with other pioneering techniques such as the MOPSO
多目标引力搜索算法
近年来,人们对多目标群优化算法的各种变体进行了大量的研究,每种算法都有自己的优缺点。由于多目标问题的高度复杂性,这些方法的效率成为人们关注的问题。本文提出了一种基于重力的多目标元启发式算法,并将其应用于不同的试验台。仿真结果表明,该算法相对于MOPSO等先进算法具有一定的优越性
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:481959085
Book学术官方微信