Research of Multi-objective Optimization Based on Hybrid Genetic Algorithm

Hua Jiang, GuiLin Xu, Zhenrong Deng
{"title":"Research of Multi-objective Optimization Based on Hybrid Genetic Algorithm","authors":"Hua Jiang, GuiLin Xu, Zhenrong Deng","doi":"10.1109/NCM.2009.136","DOIUrl":null,"url":null,"abstract":"In the process of solving multi-objective Pareto solution, the search ability in total area and the convergence characteristics can be reinforced by self-adjusting of aberrance probability in offspring evolution. Comparing with the typical hybrid genetic algorithm, the more effective optimization convergence can be obtained by using the improved hybrid genetic algorithm in solution for optimization problem. Numerical simulation based on some typical examples demonstrate the effectiveness of the proposed method.","PeriodicalId":119669,"journal":{"name":"2009 Fifth International Joint Conference on INC, IMS and IDC","volume":"53 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 Fifth International Joint Conference on INC, IMS and IDC","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NCM.2009.136","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

Abstract

In the process of solving multi-objective Pareto solution, the search ability in total area and the convergence characteristics can be reinforced by self-adjusting of aberrance probability in offspring evolution. Comparing with the typical hybrid genetic algorithm, the more effective optimization convergence can be obtained by using the improved hybrid genetic algorithm in solution for optimization problem. Numerical simulation based on some typical examples demonstrate the effectiveness of the proposed method.
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
基于混合遗传算法的多目标优化研究
在求解多目标Pareto解的过程中,通过对后代进化过程中异常概率的自调节,增强了算法的总体搜索能力和收敛特性。与典型的混合遗传算法相比,改进的混合遗传算法在求解优化问题时可以获得更有效的优化收敛性。通过典型算例的数值仿真,验证了该方法的有效性。
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
Based on Improved BP Neural Network to Forecast Demand for Spare Parts Integrated Network Management Certification Training with Computer Game: A Knowledge Placement Framework Improving Scalability for RFID Privacy Protection Using Parallelism A Brand-New Mobile Value-Added Service: M-Check A Uniform Construction of New Exact Travelling Wave Solutions and its Applications
×
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