关键词: 混合蛙跳算法, 早熟收敛, 随机扰动, 全局优化

Abstract: To solve the premature convergence problem of the Shuffled Frog Leaping Algorithm, having weak local searching ability, an improved shuffled frog algorithm is proposed. New algorithm introduces random mutations in pairs of each frog and lets the subgroup within every frog individuals involve in producing new individual, making full use of every frog individual information, increasing the diversity of population, improving global optimization ability and avoiding algorithm to fall into local convergence. The simulation shows that the improved shuffled frog leaping algorithm effectively avoids falling into local convergence, improving the convergence precision.

Key words: shuffled frog leaping algorithm, premature convergence, random mutations, global optimization