南京信息工程大学学报2023，Vol.15Issue(6)：662-675,14.DOI:10.13878/j.cnki.jnuist.20221215003
多策略融合的黄金正弦樽海鞘群算法
Golden sine salp swarm algorithm with multi-strategy
摘要
Abstract
To improve the poor convergence performance and escape from local optimum of Salp Swarm Algorithm(SSA),a Golden sine SSA with Multi-strategy(MGSSA)is proposed.First,the Selective Opposition-Based Learning(SOBL)strategy is used to improve the population quality by calculating selective opposite solutions for individuals in the population that completely deviate from the optimal individual search direction.Then the optimal individual and elite mean individual are added in the follower position update phase to speed up the convergence of the algorithm.Finally,the golden sine algorithm variation strategy is selected based on the probability to further im-prove the quality of the solution,and facilitate the algorithm to jump out of the local optimum later.In this study,ex-periments are conducted on 14 benchmark test functions to compare with other swarm intelligence optimization algo-rithms and novel improved SSA,and then the proposed approach is applied to test the solution of engineering optimi-zation problems in tension/compression spring design.The results show that the proposed MGSSA has high conver-gence accuracy and stability,and performs well in solving engineering problems.关键词
樽海鞘群算法/选择反向学习/精英均值/黄金正弦算法Key words
salp swarm algorithm(SSA)/selective opposition-based learning(SOBL)/elite mean/golden sine al-gorithm分类
信息技术与安全科学引用本文复制引用
丁美芳,吴克晴,肖鹏..多策略融合的黄金正弦樽海鞘群算法[J].南京信息工程大学学报,2023,15(6):662-675,14.基金项目
国家自然科学基金(61364015) （61364015）
