{"title":"Chicken Swarm Optimization Algorithm Based on Adaptive Dynamic Distribution","authors":"Xinxin Zhou, Zhirui Gao, Xueting Yi, Daheng Lin","doi":"10.1109/CICN51697.2021.9574678","DOIUrl":null,"url":null,"abstract":"Aiming at the problem of low accuracy of the Chicken Swarm Optimization Algorithm and falling into the local optimum easily, a self-adaptive dynamic distribution Chicken Swarm Optimization (DCSO) is proposed. Firstly, a dynamic weight strategy is proposed to solve the problem of reduced algorithm accuracy; Secondly, the learning factor of normal distribution is used to solve the problem that the algorithm is easy to fall into the local optimum; Finally, 16 benchmark functions are used to test the performance of the algorithm. And the experimental results show that the improved Chicken Swarm Optimization has better solution accuracy and it can jump out of the local optimum.","PeriodicalId":224313,"journal":{"name":"2021 13th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 13th International Conference on Computational Intelligence and Communication Networks (CICN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CICN51697.2021.9574678","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
Aiming at the problem of low accuracy of the Chicken Swarm Optimization Algorithm and falling into the local optimum easily, a self-adaptive dynamic distribution Chicken Swarm Optimization (DCSO) is proposed. Firstly, a dynamic weight strategy is proposed to solve the problem of reduced algorithm accuracy; Secondly, the learning factor of normal distribution is used to solve the problem that the algorithm is easy to fall into the local optimum; Finally, 16 benchmark functions are used to test the performance of the algorithm. And the experimental results show that the improved Chicken Swarm Optimization has better solution accuracy and it can jump out of the local optimum.
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