Adjustable mode ratio and focus boost search strategy for cat swarm optimization

Pei-wei Tsai, Xingsi Xue, Jing Zhang, V. Istanda
{"title":"Adjustable mode ratio and focus boost search strategy for cat swarm optimization","authors":"Pei-wei Tsai, Xingsi Xue, Jing Zhang, V. Istanda","doi":"10.3934/aci.2021005","DOIUrl":null,"url":null,"abstract":"Evolutionary algorithm is one of the optimization techniques. Cat swarm optimization (CSO)-based algorithm is frequently used in many applications for solving challenging optimization problems. In this paper, the tracing mode in CSO is modified to reduce the number of user-defined parameters and weaken the sensitivity to the parameter values. In addition, a mode ratio control scheme for switching individuals between different movement modes and a search boosting strategy are proposed. The obtained results from our method are compared with the modified CSO without the proposed strategy, the original CSO, the particle swarm optimization (PSO) and differential evolution (DE) with three commonly-used DE search schemes. Six test functions from IEEE congress on evolutionary competition (CEC) are used to evaluate the proposed methods. The overall performance is evaluated by the average ranking over all test results. The ranking result indicates that our proposed method outperforms the other methods compared.","PeriodicalId":414924,"journal":{"name":"Applied Computing and Intelligence","volume":"24 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Computing and Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3934/aci.2021005","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Evolutionary algorithm is one of the optimization techniques. Cat swarm optimization (CSO)-based algorithm is frequently used in many applications for solving challenging optimization problems. In this paper, the tracing mode in CSO is modified to reduce the number of user-defined parameters and weaken the sensitivity to the parameter values. In addition, a mode ratio control scheme for switching individuals between different movement modes and a search boosting strategy are proposed. The obtained results from our method are compared with the modified CSO without the proposed strategy, the original CSO, the particle swarm optimization (PSO) and differential evolution (DE) with three commonly-used DE search schemes. Six test functions from IEEE congress on evolutionary competition (CEC) are used to evaluate the proposed methods. The overall performance is evaluated by the average ranking over all test results. The ranking result indicates that our proposed method outperforms the other methods compared.
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
猫群优化的可调模式比和聚焦增强搜索策略
进化算法是一种优化技术。基于Cat群优化(CSO)的算法在许多应用中经常用于解决具有挑战性的优化问题。本文对CSO中的跟踪模式进行了改进,减少了自定义参数的数量,减弱了对参数值的敏感性。此外,提出了一种用于个体在不同运动模式之间切换的模式比控制方案和搜索增强策略。将该方法得到的结果与不采用该策略的改进CSO、原始CSO、粒子群优化(PSO)和差分进化(DE)三种常用DE搜索方案进行了比较。采用IEEE进化竞争大会(CEC)的六个测试函数对所提出的方法进行了评价。总体性能通过对所有测试结果的平均排名来评估。排序结果表明,本文提出的方法优于其他方法。
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
Novel split quality measures for stratified multilabel cross validation with application to large and sparse gene ontology datasets Crop and weed classification based on AutoML A review of the application of machine learning in adult obesity studies Definition modeling: literature review and dataset analysis Effects of COVID-19 pandemic on computational intelligence and cybersecurity: survey
×
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