A New Evolutionary Algorithm Based on Simplex Crossover and PSO Mutation for Constrained Optimization Problems

Yifan Hu
{"title":"A New Evolutionary Algorithm Based on Simplex Crossover and PSO Mutation for Constrained Optimization Problems","authors":"Yifan Hu","doi":"10.1109/CIS.2010.38","DOIUrl":null,"url":null,"abstract":"A new approach is presented to handle constraints optimization using evolutionary algorithms in this paper. First, we present a specific varying fitness function technique, this technique incorporates the problem’s constraints into the fitness function in a dynamic way. The resulting varying fitness function facilitates the EA search. On one hand, The new fitness function without any parameters can properly evaluate not only feasible solution, but also infeasible one, on other hand, the information of the best solution in the current population is also concerned in fitness function, which make search more efficient. Meanwhile, a new crossover operator based on simplex crossover operator and a new PSO mutation operator is also proposed, and both the operators utilize the information of good individuals in the current populations so they can produce high quality offspring. Based on these, a new evolutionary algorithm for constrained optimization problems is proposed. The simulations are made on five widely used benchmark problems, and the results indicate the proposed algorithm is effective","PeriodicalId":420515,"journal":{"name":"2010 International Conference on Computational Intelligence and Security","volume":"75 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 International Conference on Computational Intelligence and Security","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CIS.2010.38","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

A new approach is presented to handle constraints optimization using evolutionary algorithms in this paper. First, we present a specific varying fitness function technique, this technique incorporates the problem’s constraints into the fitness function in a dynamic way. The resulting varying fitness function facilitates the EA search. On one hand, The new fitness function without any parameters can properly evaluate not only feasible solution, but also infeasible one, on other hand, the information of the best solution in the current population is also concerned in fitness function, which make search more efficient. Meanwhile, a new crossover operator based on simplex crossover operator and a new PSO mutation operator is also proposed, and both the operators utilize the information of good individuals in the current populations so they can produce high quality offspring. Based on these, a new evolutionary algorithm for constrained optimization problems is proposed. The simulations are made on five widely used benchmark problems, and the results indicate the proposed algorithm is effective
约束优化问题中一种基于单纯形交叉和粒子群变异的进化算法
提出了一种利用进化算法处理约束优化问题的新方法。首先,我们提出了一种具体的变适应度函数技术,该技术以动态的方式将问题的约束引入到适应度函数中。由此产生的可变适应度函数便于EA搜索。一方面,不带参数的新适应度函数既能正确评估可行解,也能正确评估不可行解,另一方面,适应度函数还考虑了当前种群中最优解的信息,提高了搜索效率。同时,提出了一种新的基于单纯形交叉算子和PSO突变算子的交叉算子,这两种算子都利用了种群中优秀个体的信息来产生高质量的后代。在此基础上,提出了一种求解约束优化问题的进化算法。对5个常用的基准问题进行了仿真,结果表明该算法是有效的
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