{"title":"具有非均匀变异的多策略麻雀搜索算法","authors":"Zuwei Huang, Donglin Zhu, Yujia Liu, Xiao Wang","doi":"10.1080/21642583.2022.2140723","DOIUrl":null,"url":null,"abstract":"Sparrow search algorithm (SSA) suffers from a tendency to fall into local optima, as well as a preference for zero locations. Therefore, to improve this drawback, we propose a non-uniform mutation sparrow search algorithm (NMSSA). In the initialization stage of the population, we introduce a tent chaos map and a generalized opposition-based learning strategy to improve the diversity of the population; We introduce adaptive weight to dynamically adjust the search range of the discoverer to improve the search efficiency of the algorithm; To prevent the algorithm from deviating from the target in the early stage, we adopt a non-uniform mutation strategy to improve the flexibility of the follower search to improve the convergence accuracy of the algorithm. Finally, we use the somersault strategy to reduce the probability of the algorithm falling into local optimum. In the test experiments with 10 benchmark functions and CEC2017 functions, we compare the experimental results of NMSSA with those of other algorithms, and the experimental results verify the effectiveness of NMSSA. In addition, we also applied NMSSA to engineering problems optimization and K-means image segmentation, and the experimental results show that NMSSA has good performance in practical applications.","PeriodicalId":46282,"journal":{"name":"Systems Science & Control Engineering","volume":"10 1","pages":"936 - 954"},"PeriodicalIF":3.2000,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Multi-strategy sparrow search algorithm with non-uniform mutation\",\"authors\":\"Zuwei Huang, Donglin Zhu, Yujia Liu, Xiao Wang\",\"doi\":\"10.1080/21642583.2022.2140723\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Sparrow search algorithm (SSA) suffers from a tendency to fall into local optima, as well as a preference for zero locations. Therefore, to improve this drawback, we propose a non-uniform mutation sparrow search algorithm (NMSSA). In the initialization stage of the population, we introduce a tent chaos map and a generalized opposition-based learning strategy to improve the diversity of the population; We introduce adaptive weight to dynamically adjust the search range of the discoverer to improve the search efficiency of the algorithm; To prevent the algorithm from deviating from the target in the early stage, we adopt a non-uniform mutation strategy to improve the flexibility of the follower search to improve the convergence accuracy of the algorithm. Finally, we use the somersault strategy to reduce the probability of the algorithm falling into local optimum. In the test experiments with 10 benchmark functions and CEC2017 functions, we compare the experimental results of NMSSA with those of other algorithms, and the experimental results verify the effectiveness of NMSSA. In addition, we also applied NMSSA to engineering problems optimization and K-means image segmentation, and the experimental results show that NMSSA has good performance in practical applications.\",\"PeriodicalId\":46282,\"journal\":{\"name\":\"Systems Science & Control Engineering\",\"volume\":\"10 1\",\"pages\":\"936 - 954\"},\"PeriodicalIF\":3.2000,\"publicationDate\":\"2022-11-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Systems Science & Control Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/21642583.2022.2140723\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Systems Science & Control Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/21642583.2022.2140723","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
Multi-strategy sparrow search algorithm with non-uniform mutation
Sparrow search algorithm (SSA) suffers from a tendency to fall into local optima, as well as a preference for zero locations. Therefore, to improve this drawback, we propose a non-uniform mutation sparrow search algorithm (NMSSA). In the initialization stage of the population, we introduce a tent chaos map and a generalized opposition-based learning strategy to improve the diversity of the population; We introduce adaptive weight to dynamically adjust the search range of the discoverer to improve the search efficiency of the algorithm; To prevent the algorithm from deviating from the target in the early stage, we adopt a non-uniform mutation strategy to improve the flexibility of the follower search to improve the convergence accuracy of the algorithm. Finally, we use the somersault strategy to reduce the probability of the algorithm falling into local optimum. In the test experiments with 10 benchmark functions and CEC2017 functions, we compare the experimental results of NMSSA with those of other algorithms, and the experimental results verify the effectiveness of NMSSA. In addition, we also applied NMSSA to engineering problems optimization and K-means image segmentation, and the experimental results show that NMSSA has good performance in practical applications.
期刊介绍:
Systems Science & Control Engineering is a world-leading fully open access journal covering all areas of theoretical and applied systems science and control engineering. The journal encourages the submission of original articles, reviews and short communications in areas including, but not limited to: · artificial intelligence · complex systems · complex networks · control theory · control applications · cybernetics · dynamical systems theory · operations research · systems biology · systems dynamics · systems ecology · systems engineering · systems psychology · systems theory