MOEA/D with guided local search: Some preliminary experimental results

Ahmad Alhindi, Qingfu Zhang
{"title":"MOEA/D with guided local search: Some preliminary experimental results","authors":"Ahmad Alhindi, Qingfu Zhang","doi":"10.1109/CEEC.2013.6659455","DOIUrl":null,"url":null,"abstract":"Multiobjective Evolutionary Algorithm based on Decomposition (MOEA/D) decomposes a multiobjective optimisation into a number of single-objective problem and optimises them in a collaborative manner. This paper investigates how to use the Guided Local Search (GLS), a well-studied single objective heuristic to enhance MOEA/D performance. In our proposed approach, the GLS applies to these subproblems to escape local Pareto optimal solutions. The experimental studies have shown that MOEA/D with GLS outperforms the classical MOEA/D on a bi-objective travelling salesman problem.","PeriodicalId":309053,"journal":{"name":"2013 5th Computer Science and Electronic Engineering Conference (CEEC)","volume":"158 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 5th Computer Science and Electronic Engineering Conference (CEEC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CEEC.2013.6659455","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 10

Abstract

Multiobjective Evolutionary Algorithm based on Decomposition (MOEA/D) decomposes a multiobjective optimisation into a number of single-objective problem and optimises them in a collaborative manner. This paper investigates how to use the Guided Local Search (GLS), a well-studied single objective heuristic to enhance MOEA/D performance. In our proposed approach, the GLS applies to these subproblems to escape local Pareto optimal solutions. The experimental studies have shown that MOEA/D with GLS outperforms the classical MOEA/D on a bi-objective travelling salesman problem.
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
引导局部搜索的MOEA/D:一些初步实验结果
基于分解的多目标进化算法(MOEA/D)将一个多目标优化问题分解为多个单目标问题,并以协作的方式进行优化。本文研究了如何使用导引局部搜索(GLS)来提高MOEA/D性能,这是一种研究得很好的单目标启发式算法。在我们提出的方法中,GLS应用于这些子问题以逃避局部Pareto最优解。实验研究表明,在双目标旅行商问题上,基于GLS的MOEA/D优于经典的MOEA/D。
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
Adaptive clustering based segmentation for image classification The throughput benefits of network coding for SR ARQ communication Adaptive CT image segmentation using mathematical morphology Increasing the rate of intrusion detection based on a hybrid technique A mathematical model for a GA-based dynamic excess bandwidth allocation algorithm for hybrid PON and wireless technology integrations for next generation broadband access networks
×
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:481959085
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