{"title":"基于个体分级的双子群人工蜂群算法","authors":"Zhaolu Guo , Hongjin Li , Kangshun Li","doi":"10.1016/j.eij.2024.100452","DOIUrl":null,"url":null,"abstract":"<div><p>To boost the search performance of Artificial Bee Colony (ABC) algorithm for handling some complicated optimization problems, a dual subpopulation ABC based on individual gradation (DPGABC) is presented. In DPGABC, the whole population is segmented into two subpopulations with different gradations. Then, the subpopulations respectively utilize the strategies with different characteristics as the candidate strategies. So the individuals can exploit the benefits of various strategies to optimize the search performance. Meanwhile, the dual subpopulation mechanism can maintain good population diversity while achieving good convergence performance. In addition, a knowledge-driven parameter update mechanism is designed to improve the convergence performance. The CEC2014 test set is applied for relevant experiments to validate the performance of DPGABC. From the results, DPGABC performs well on most functions.</p></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":null,"pages":null},"PeriodicalIF":5.0000,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S111086652400015X/pdfft?md5=1d97602897374278d036e61c9da410dc&pid=1-s2.0-S111086652400015X-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Dual subpopulation artificial bee colony algorithm based on individual gradation\",\"authors\":\"Zhaolu Guo , Hongjin Li , Kangshun Li\",\"doi\":\"10.1016/j.eij.2024.100452\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>To boost the search performance of Artificial Bee Colony (ABC) algorithm for handling some complicated optimization problems, a dual subpopulation ABC based on individual gradation (DPGABC) is presented. In DPGABC, the whole population is segmented into two subpopulations with different gradations. Then, the subpopulations respectively utilize the strategies with different characteristics as the candidate strategies. So the individuals can exploit the benefits of various strategies to optimize the search performance. Meanwhile, the dual subpopulation mechanism can maintain good population diversity while achieving good convergence performance. In addition, a knowledge-driven parameter update mechanism is designed to improve the convergence performance. The CEC2014 test set is applied for relevant experiments to validate the performance of DPGABC. From the results, DPGABC performs well on most functions.</p></div>\",\"PeriodicalId\":56010,\"journal\":{\"name\":\"Egyptian Informatics Journal\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.0000,\"publicationDate\":\"2024-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S111086652400015X/pdfft?md5=1d97602897374278d036e61c9da410dc&pid=1-s2.0-S111086652400015X-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Egyptian Informatics Journal\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S111086652400015X\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Egyptian Informatics Journal","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S111086652400015X","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Dual subpopulation artificial bee colony algorithm based on individual gradation
To boost the search performance of Artificial Bee Colony (ABC) algorithm for handling some complicated optimization problems, a dual subpopulation ABC based on individual gradation (DPGABC) is presented. In DPGABC, the whole population is segmented into two subpopulations with different gradations. Then, the subpopulations respectively utilize the strategies with different characteristics as the candidate strategies. So the individuals can exploit the benefits of various strategies to optimize the search performance. Meanwhile, the dual subpopulation mechanism can maintain good population diversity while achieving good convergence performance. In addition, a knowledge-driven parameter update mechanism is designed to improve the convergence performance. The CEC2014 test set is applied for relevant experiments to validate the performance of DPGABC. From the results, DPGABC performs well on most functions.
期刊介绍：
The Egyptian Informatics Journal is published by the Faculty of Computers and Artificial Intelligence, Cairo University. This Journal provides a forum for the state-of-the-art research and development in the fields of computing, including computer sciences, information technologies, information systems, operations research and decision support. Innovative and not-previously-published work in subjects covered by the Journal is encouraged to be submitted, whether from academic, research or commercial sources.
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