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
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
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 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Genetic Algorithm-Artificial Neural Network (GA-ANN) Hybrid Intelligence for Cancer Diagnosis EEG Analysis for Brainwave Balancing Index (BBI) Expert-Aware Approach: A New Approach to Improve Network Security Visualization Tool Micro SOA Model for Managing and Integrating Wireless Sensor Network into IP-Based Networks Context-Aware News Recommender in Mobile Hybrid P2P Network
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1