{"title":"Improved sine cosine algorithm for large-scale optimization problems","authors":"Chao Zhang, Yezhou Yang","doi":"10.3724/sp.j.1249.2022.06684","DOIUrl":null,"url":null,"abstract":"体局部开发和逃离局部极值的能力;采用基于空间距离的非线性参数调整方法,平衡算法的局部开发和全 局搜索,提高了算法的收敛速度.在14个经典测试函数上,维度分别为100、1 000和 5 000维时,与SCA、 花授粉算法(flower pollination algorithm,FPA)、粒子群优化(particle swarm optimization,PSO)算法、麻雀搜 索算法(sparrow search algorithm,SSA)和鲸鱼优化算法(whale optimization algorithm,WOA)5种群体智能算法 进行仿真对比实验.结果表明,SCAL算法在收敛精度、收敛速度和鲁棒性上较 5种群体智能算法优势明 显.与解决大规模优化问题的改进狼群算法(improved wolf pack algorithm,IWPA)、改进花授粉算法 (improved flower pollination algorithm,IFPA)、鲸鱼算法的两种改进版本 IWOA(improved whale optimization algorithm)和MWOA(modified whale optimization algorithm)进行比较,发现SCAL的整体寻优结果优于对比算","PeriodicalId":35396,"journal":{"name":"Shenzhen Daxue Xuebao (Ligong Ban)/Journal of Shenzhen University Science and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Shenzhen Daxue Xuebao (Ligong Ban)/Journal of Shenzhen University Science and Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3724/sp.j.1249.2022.06684","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Engineering","Score":null,"Total":0}
引用次数: 1
大规模优化问题的改进正弦余弦算法
体局部开发和逃离局部极值的能力;采用基于空间距离的非线性参数调整方法,平衡算法的局部开发和全 局搜索,提高了算法的收敛速度.在14个经典测试函数上,维度分别为100、1 000和 5 000维时,与SCA、 花授粉算法(flower pollination algorithm,FPA)、粒子群优化(particle swarm optimization,PSO)算法、麻雀搜 索算法(sparrow search algorithm,SSA)和鲸鱼优化算法(whale optimization algorithm,WOA)5种群体智能算法 进行仿真对比实验.结果表明,SCAL算法在收敛精度、收敛速度和鲁棒性上较 5种群体智能算法优势明 显.与解决大规模优化问题的改进狼群算法(improved wolf pack algorithm,IWPA)、改进花授粉算法 (improved flower pollination algorithm,IFPA)、鲸鱼算法的两种改进版本 IWOA(improved whale optimization algorithm)和MWOA(modified whale optimization algorithm)进行比较,发现SCAL的整体寻优结果优于对比算
本文章由计算机程序翻译，如有差异，请以英文原文为准。
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
