{"title":"A Fuzzy Rule-Based Krill Herd Algorithm","authors":"Fang Su, Wenzhe Yang, C. Duan, Jilong Li","doi":"10.1109/ICCC47050.2019.9064372","DOIUrl":null,"url":null,"abstract":"Standard Krill Herd (SKH) optimization algorithm is a novel heuristic optimization algorithm, and its control parameters play an important role for its performance. In this paper, an improved Krill Herd algorithm is proposed, in which the fuzzy system is utilized as the parameter tuner to adjust control parameters by observing the progress of solving the problem in each step. The innovation is that both scaling factor and inertia weight are considered, and these parameters can be adjusted automatically according to the particle situation. In order to evaluate the proposed FKH algorithm, the efficiency of FKH algorithm is verified by using 16 benchmark functions, the results indicate the superiority of proposed FKH optimization algorithm in comparison with the standard KH.","PeriodicalId":6739,"journal":{"name":"2019 IEEE 5th International Conference on Computer and Communications (ICCC)","volume":"91 8 1","pages":"61-65"},"PeriodicalIF":0.0000,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE 5th International Conference on Computer and Communications (ICCC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCC47050.2019.9064372","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
Standard Krill Herd (SKH) optimization algorithm is a novel heuristic optimization algorithm, and its control parameters play an important role for its performance. In this paper, an improved Krill Herd algorithm is proposed, in which the fuzzy system is utilized as the parameter tuner to adjust control parameters by observing the progress of solving the problem in each step. The innovation is that both scaling factor and inertia weight are considered, and these parameters can be adjusted automatically according to the particle situation. In order to evaluate the proposed FKH algorithm, the efficiency of FKH algorithm is verified by using 16 benchmark functions, the results indicate the superiority of proposed FKH optimization algorithm in comparison with the standard KH.
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