趋优变异反向学习的樽海鞘群与蝴蝶混合优化算法.

To address the problems of the butterfly optimization algorithm, which include vulnerability to local optima,low optimization accuracy and slow convergence speed. This paper proposed a hybrid optimization algorithm for salp swarm and butterfly with reverse mutation towards optimization learning. The algorithm introduced Cauchy mutation to disturb the optimal butterfly individual to avoid the algorithm falling into local optimization. Embedding the improved salp swarm algorithm (SSA) into BOA adjust the proportion of global exploration and local mining, thereby enhancing the algorithm’s convergence speed. Using the reverse mutation towards optimization learning strategy enhanced the algorithm’s search space and improved the quality of solutions, consequently bolstering its overall optimization accuracy. The experimental results, obtained from conducting simulations on 10 benchmark functions,showcase the exceptional optimization performance and robustness of the improved algorithm. [ABSTRACT FROM AUTHOR]