Advances in Shuffled Frog Leaping Algorithm
Jiafu Tang · 2010
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 implemented.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.