关键词: 蝴蝶优化算法, 随机一致性初始化, 差分进化算法, 基准函数, Wilcoxon秩和检验

Abstract: Aiming at the shortcomings of the butterfly optimization algorithm (BOA),such as poor search accuracy,imbalanced global exploration and local exploitation capabilities,and susceptibility to local optima,this paper proposes a multi-strategy improved butterfly optimization algorithm (MSIBOA) to enhance its robustness and optimization performance.The improved algorithm adopts random consistency to initialize the butterfly population,ensuring a more uniform distribution of individuals across all dimensions of the search space and broader coverage of the solution space.Dynamic inertia weight strategy is introduced to balance global and local search,while an elite differential mutation strategy is incorporated to boost the algorithm's global search capability.Experimental comparisons between the improved algorithm and seven other optimization algorithms on 17 benchmark functions demonstrate that MSIBOA outperforms the original BOA in convergence speed,solution accuracy,global optimization capability,and robustness.

Key words: butterfly optimization algorithm(BOA), random consistency initialization, differential evolution(DE), benchmark function, Wilcoxon rank sum test