多策略协同的改进鲸鱼优化算法

An improved whale optimization algorithm based on multi-strategy coordination

针对鲸鱼优化算法求解精度低、收敛速度慢和易陷入局部最优等问题,提出一种多策略协同的改进鲸鱼优化算法(MSWOA)。首先利用种群信息引导机制提升全局最优位置的开采效率,避免算法在迭代后期陷入局部最优;其次将改进的黄金正弦算法融入鲸鱼包围猎物的过程,以扩大种群在解空间内的搜索范围;最后采用惯性权重和非线性参数调整策略提升算法的全局探索和局部开发能力。通过对不同改进策略的有效性分析、与其他智能算法的对比分析、高维情形下的寻优性能分析、Wilcoxon秩和检验,证明了MSWOA算法具有更好的寻优精度和稳定性。

To solve the problems of whale optimization algorithm,such as low precision,slow convergence speed and ease of falling into local optimal,a multi-strategy collaborative improved whale optimization algorithm(MSWOA)is proposed.Firstly,the population information guidance mechanism is used to improve the mining efficiency of the global optimal position,so as to avoid that the algorithm falls into the local optimal position in the late iteration.Secondly,the improved golden sine algorithm is combined with the process of whale encircling prey to enlarge the search range of the population in the solution space.Finally,the inertial weight and nonlinear parameter adjustment strategy are used to improve the global exploration and local development ability of the algorithm.Through the effectiveness analysis of different improved strategies,comparison analysis with other intelligent algorithms,optimization performance analysis in high-dimensional cases,and Wilcoxon rank sum test,it is proved that MSWOA algorithm has better optimization accuracy and stability.