{"title":"一种新的正弦余弦全局优化算法","authors":"Yuan xia Shen, Chuan hua Zeng, Xiao yan Wang","doi":"10.1145/3507548.3507579","DOIUrl":null,"url":null,"abstract":"Sine cosine algorithm (SCA) has a fast convergence speed and is easy to implement. In order to overcome the evolutionary stagnation of swarm, this paper presents a novel SCA (NSCA) in which three learning strategies are used to update individuals and a selection mechanism is developed to guide each individual to choose a proper updating strategy. The selection mechanism is designed by the credit assignment method and Upper Confidence Bound (UCB). The proposed algorithm has been experimentally validated on 18 benchmark functions. Compared with SCA variants and other swarm intelligence algorithms, experimental results show NSCA is competitive in solving most functions.","PeriodicalId":414908,"journal":{"name":"Proceedings of the 2021 5th International Conference on Computer Science and Artificial Intelligence","volume":"5 6part2","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A Novel Sine Cosine Algorithm for Global Optimization\",\"authors\":\"Yuan xia Shen, Chuan hua Zeng, Xiao yan Wang\",\"doi\":\"10.1145/3507548.3507579\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Sine cosine algorithm (SCA) has a fast convergence speed and is easy to implement. In order to overcome the evolutionary stagnation of swarm, this paper presents a novel SCA (NSCA) in which three learning strategies are used to update individuals and a selection mechanism is developed to guide each individual to choose a proper updating strategy. The selection mechanism is designed by the credit assignment method and Upper Confidence Bound (UCB). The proposed algorithm has been experimentally validated on 18 benchmark functions. Compared with SCA variants and other swarm intelligence algorithms, experimental results show NSCA is competitive in solving most functions.\",\"PeriodicalId\":414908,\"journal\":{\"name\":\"Proceedings of the 2021 5th International Conference on Computer Science and Artificial Intelligence\",\"volume\":\"5 6part2\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-12-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2021 5th International Conference on Computer Science and Artificial Intelligence\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3507548.3507579\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2021 5th International Conference on Computer Science and Artificial Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3507548.3507579","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Novel Sine Cosine Algorithm for Global Optimization
Sine cosine algorithm (SCA) has a fast convergence speed and is easy to implement. In order to overcome the evolutionary stagnation of swarm, this paper presents a novel SCA (NSCA) in which three learning strategies are used to update individuals and a selection mechanism is developed to guide each individual to choose a proper updating strategy. The selection mechanism is designed by the credit assignment method and Upper Confidence Bound (UCB). The proposed algorithm has been experimentally validated on 18 benchmark functions. Compared with SCA variants and other swarm intelligence algorithms, experimental results show NSCA is competitive in solving most functions.
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