随机蛙跳算法的研究进展

Advances in Shuffled Frog Leaping Algorithm

随机蛙跳算法(Shuffled Frog Leaping Lgorithm,SFLA)是进化计算领域中一种新兴、有效的亚启发式群体计算技术,近几年来逐渐受到学术界和工程优化领域的关注。SFLA结合了具有较强局部搜索(Local Search,LS)能力的元算法(Memetic Algorithm,MA)和具有良好全局搜索(Global Search,GS)性能的粒子群算法(Particle Swarm Optimization,PSO)的特点,因此其寻优能力强,易于编程实现。详细阐述了SFLA的基本原理和流程,总结了SFLA目前在优化和工程技术等领域中的研究,展望了SFLA的发展前景。

Shuffled Frog Leaping Algorithm （SFLA） is a population-based novel and effective meta-heuristics computing method,which received increasing focuses from academic and engineering optimization fields in recent years. Since SFLA is a combination of Memetic Algorithm （MA） with strong Local Search （LS） ability and Particle Swarm Optimization （PSO） with good Global Search （GS） capability, it is of strong optimum-searching power and easy to be implementecl. In this paper, the fundamental principles and framework of SFLA were described. Then, the related researches of SFLA in the current optimization and engineering fields were summed up. Lastly, the future perspectives of SFLA were presented.