Abstract:

In order to solve the problems of slow convergence speed, low accuracy and easy to fall into local optimum solution of the standard salp swarm algorithm, an adaptive salp swarm algorithm based on golden sine algorithm and hybrid mutation is proposed. The adaptive weight factor is introduced to strengthen the leading role of elite individuals and improve the convergence speed and accuracy of the basic salp swarm algorithm. The golden sine algorithm is used to optimize the position update mode of the leader and improve the global exploration and local exploitation capacity of the algorithm. The hybrid neighborhood centroid opposition-based learning with cauchy mutation strategy is introduced to disturb the best individual’s position and improve the ability of the algorithm to jump out of local optimum. The optimization performance of the proposed algorithm is evaluated by a sets of simulation experiments on 12 benchmark functions, the experimental results show that the proposed algorithm can significantly improve the optimum speed and accuracy, besides, it has a strong ability to jump out of local optimum.

Key words: salp swarm algorithm, adaptive weight, golden sine algorithm, hybrid mutation

摘要：

针对基本樽海鞘群算法收敛速度慢、收敛精度低、易陷入局部最优的缺点，提出了一种融合黄金正弦混合变异的自适应樽海鞘群算法AGHSSA（Adaptive Salp Swarm Algorithm with Golden Sine Algorithm and Hybrid Mutation）。该算法引入了自适应变化的权重因子以加强精英个体的引导作用，提升收敛速度与精度。通过黄金正弦算法优化领导者位置更新方式，增强算法的全局搜索和局部开发能力。融合邻域重心反向学习与柯西变异对最优个体位置进行扰动，提升算法跳出局部最优的能力。通过对12个基准测试函数进行仿真实验来评估改进算法的寻优能力，实验结果表明，改进算法能显著提升寻优速度和精度，并且具备较强的跳出局部最优的能力。

关键词: 樽海鞘群算法, 自适应权重, 黄金正弦算法, 混合变异