内嵌扰动变异的混合蛙跳算法

Disturbance variation embedded shuffled frog leaping algorithm

针对蛙跳算法局部搜索能力较弱,容易陷入早熟收敛的现象,提出了一种改进的混合蛙跳算法。新算法对子群中每只新青蛙个体引入了随机扰动,并让子群内每只青蛙个体都参与产生新个体,充分利用每只青蛙个体的信息,增加了种群的多样性,提升算法的全局寻优能力,从而避免算法陷入局部收敛。实验表明,改进的混合蛙跳算法有效避免算法陷入局部收敛,提升了算法的收敛精度。

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.