关键词: 混合蛙跳算法, 免疫算法, 自适应, 函数优化

Abstract: As basic Shuffled Frog Leaping Algorithm（SFLA） has many problems in the high dimensional multimodal function optimization such as premature and difficult to find all global extremes, a hybrid intelligent Polymorphic Adaptive Shuffled Frog Leaping Immune Algorithm（PASFLIA） is presented. PASFLIA which combines the SFLA and immune Clonal Selection Algorithm（CSA） technology is convergent to the global optimal solution with probability 1. PASFLIA adopts a double-layer model evolution: in the lower layer, the polymorphic adaptive population mechanism is joined to SFLA, which improves the diversity of the population, and effectively suppresses premature behavior of the convergence progress; in the higher layer, the global extreme screening strategy is proposed, which puts the population extreme points to high-level immune clonal selection operation, further enhancing the global optimization ability. The complex multimodal function simulation experiments results show that the PASFLIA can quickly and efficiently give all the global optimal solution.

Key words: shuffled frog leaping algorithm, immune algorithm, adaptive, function optimization