{"title":"A Novel Cooperative Parallel Multi-Population Optimization Algorithm","authors":"N. Verma, Pooya Moradian Zadeh, Ziad Kobti","doi":"10.1145/3571697.3571711","DOIUrl":null,"url":null,"abstract":"This work proposes a new parallel meta-heuristic optimization algorithm to deal with high dimensional optimization problems. We introduce a parallel and co-evolving multi population framework that mimics the hierarchical structure of grey wolves. We also propose using elite groups and a probabilistic mutation operator to improve the convergence speed and exploration ability. The algorithm is benchmarked on the twenty-eight functions of IEEE Congress of Evolutionary Computation (CEC) 2013 test suites and is compared with other meta-heuristic algorithms. Our proposed algorithm results show that our algorithm can find more optimal solutions at higher dimensions as compared to other meta-heuristic algorithms. Non-parametric statistical test also show the consistency in the obtained results.","PeriodicalId":400139,"journal":{"name":"Proceedings of the 2022 European Symposium on Software Engineering","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2022 European Symposium on Software Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3571697.3571711","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
This work proposes a new parallel meta-heuristic optimization algorithm to deal with high dimensional optimization problems. We introduce a parallel and co-evolving multi population framework that mimics the hierarchical structure of grey wolves. We also propose using elite groups and a probabilistic mutation operator to improve the convergence speed and exploration ability. The algorithm is benchmarked on the twenty-eight functions of IEEE Congress of Evolutionary Computation (CEC) 2013 test suites and is compared with other meta-heuristic algorithms. Our proposed algorithm results show that our algorithm can find more optimal solutions at higher dimensions as compared to other meta-heuristic algorithms. Non-parametric statistical test also show the consistency in the obtained results.