关键词: 混合蛙跳算法, 智能优化, 早熟收敛, 差分进化, 扰动

Abstract: Basic Shuffled Frog Leaping Algorithm (SFLA) algorithm easily traps into local optimum and has a low convergent precision when being used to address complex functions. To overcome these above shortcomings, an improved SFLA based on mutation idea in Differential Evolution (DE) was proposed. The proposed algorithm used beneficial information of the other individuals in sub-group to disturb updating strategy locally. The experimental results show that the improved SFLA has a better capability to solve complex functions than other algorithms. It has high optimization efficiency, good global performance, and stable optimization outcomes, and is superior to the other algorithms.

Key words: Shuffled Frog Leaping Algorithm (SFLA), intelligent optimization, premature convergence, Differential Evolution (DE), disturbance