多策略融合的黄金正弦樽海鞘群算法

针对樽海鞘群算法（Salp Swarm Algorithm，SSA）收敛性能差、容易陷入局部最优等问题，提出了多策略融合的黄金正弦樽海鞘群算法（Golden sine Salp Swarm Algorithm with Multi-strategy，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；然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其他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.

Golden sine salp swarm algorithm with multi-strategy

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 improve the quality of the solution, and facilitate 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 novel improved SSA, and then the proposed approach is applied to test the solution of engineering optimization problems in tension/compression spring design.The results show that the proposed MGSSA has high convergence accuracy and stability, and performs well in solving engineering problems.