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

摘要： 针对基本混合蛙跳算法在处理复杂函数优化问题时容易陷入局部最优、求解精度低的缺点，借鉴差分进化中的变异思想，提出了一种改进的混合蛙跳算法，利用子群中其他个体的有利信息，对其更新策略进行局部扰动。实验结果表明，改进的混合蛙跳算法对复杂函数优化问题具有较强的求解能力。算法寻优效率高、全局性能好、优化结果稳定，性能明显优于所比较的算法。

关键词: 混合蛙跳算法, 智能优化, 早熟收敛, 差分进化, 扰动