关键词: 鲸鱼优化算法, 种群信息引导, 黄金正弦算法, 自适应权重, 寻优精度

Abstract: 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.

Key words: whale optimization algorithm, population information guidance, gold sine algorithm, adaptive weight, optimization accuracy