关键词: 帝王蝶优化算法, 变异反向学习, 自适应策略, 柯西变异

Abstract: Aiming at the problem that the original monarch butterfly optimization algorithm is easy to fall into the local optimal solution and has poor convergence, an adaptive monarch butterfly optimization algorithm based on mutation reverse learning is proposed. Firstly, the mutation idea of genetic algorithm is combined with the reverse learning strategy to replace the original transfer operator to improve the global convergence. Then, an adaptive strategy is integrated into the adjustment operator of the original monarch butterfly optimization algorithm to make the population more diverse. Finally, Cauchy mutation will be carried out among the last five monarch butterflies in the updated population, which will generate greater disturbance near the mutated individuals and make the whole population search for optimization in a larger range. In order to verify the improved monarch butterfly optimization algorithm, benchmark function and Wilcoxon rank sum test are used to test it. The experimental results show that the convergence speed and optimization accuracy of the improved algorithm are greatly improved.

Key words: monarch butterfly optimization algorithm, variation reverse learning, adaptive strategy, Cauchy variation