{"title":"Novel Multi-Strategy Enhanced Whale Optimization Algorithm","authors":"Zong-Sing Huang, Wan-Ling Li","doi":"10.1109/ECICE50847.2020.9301990","DOIUrl":null,"url":null,"abstract":"Whale Optimization Algorithm (WOA) is presented recently the state-of-the-art meta-heuristic optimization algorithm which has the critical advantages of fewer hyperparameters and simple framework. Unfortunately, WOA is not suitable to solve multimodal problems because of slow convergence. This paper proposes a novel multi-strategy enhanced whale optimization algorithm (MSEWOA) in order to improve WOA deal with multimodal ability. This paper has been completed testing with 23 benchmark functions. In experiments on multimodal problems with MSEWOA, it performed more effective than WOA and other conventional methods.","PeriodicalId":130143,"journal":{"name":"2020 IEEE Eurasia Conference on IOT, Communication and Engineering (ECICE)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE Eurasia Conference on IOT, Communication and Engineering (ECICE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ECICE50847.2020.9301990","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
Whale Optimization Algorithm (WOA) is presented recently the state-of-the-art meta-heuristic optimization algorithm which has the critical advantages of fewer hyperparameters and simple framework. Unfortunately, WOA is not suitable to solve multimodal problems because of slow convergence. This paper proposes a novel multi-strategy enhanced whale optimization algorithm (MSEWOA) in order to improve WOA deal with multimodal ability. This paper has been completed testing with 23 benchmark functions. In experiments on multimodal problems with MSEWOA, it performed more effective than WOA and other conventional methods.