关键词: 蝴蝶优化算法, 自适应权重, 柯西变异, 动态切换概率, 高维

Abstract:

Basic Butterfly Optimization Algorithm（BOA）, which has low convergence precision and easy to fall into the local optimal solution. Cauchy variation and adaptive Weight Butterfly Optimization Algorithm（CWBOA） is proposed. By introducing the Cauchy distribution function at the global location update, the adaptive weighting factor is introduced at the local location update to improve the local search ability of the butterfly algorithm, and the dynamic handover probability [p] is introduced to weigh the proportion of the local mining and global search process. This paper improves the algorithm by solving multiple single-peak, multi-peak and fixed test dimension functions. The results show that CWBOA has better solution accuracy, speed and stability for most test functions.

Key words: butterfly optimization algorithm, adaptive weight, Cauchy variation, dynamic switching probability, high-dimensional