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多策略融合的黄金正弦樽海鞘群算法
Golden Sine Salp Swarm Algorithm with multi-strategy
为改善樽海鞘群算法（SSA）收敛性能差，容易陷入局部最优的问题，提出了多策略融合的黄金正弦樽海鞘群算法（MGSSA）。首先，采用选择反向学习策略对种群中完全偏离最优个体寻优方向的个体计算选择反向解，改善种群质量。之后在跟随者位置更新阶段加入最优个体和精英均值个体引导，以加快算法收敛速度。最后根据概率选择黄金正弦算法变异策略，进一步改善解的质量，同时便于算法后期跳出局部最优。本研究在14个基准测试函数上进行实验，与其他群智能优化算法和新进改进樽海鞘群算法对比，将其应用于拉压弹簧设计问题测试解决工程优化问题的性能。结果表明：MGSSA具有较高的收敛精度和稳定性，在求解工程问题时性能良好。
In order to improve the problem of poor convergence performance and easy to fall into local optimum of SSA, the golden sine salp swarm algorithm (MGSSA) with multi-strategy is proposed. First, the selection opposition strategy is used to improve the population quality by calculating selection opposite solutions for individuals in the population that completely deviate from the optimal individual search direction. After that, 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 improve the quality of the solution, while facilitating the algorithm to jump out of the local optimum later. In this study, experiments are conducted on 14 benchmark test functions to compare with other swarm intelligence optimization algorithms and the novel improved salp swarm algorithms, and its performance is applied to test the solution of engineering optimization problems in tension-pressure spring design problems. The results show that MGSSA has high convergence accuracy and stability, and performs well in solving engineering problems.
樽海鞘群算法；选择反向学习；精英均值；黄金正弦算法
salp swarm algorithm; selective opposition; elite mean; golden sine algorithm
丁美芳,吴克晴,肖鹏
Ding Meifang,WU Keqing,Xiao Peng
江西理工大学
Jiangxi University of Science and Technology
njqxxyxb/article/abstract/20221215003