A Modified Shuffled Frog Leaping Algorithm with Convergence of Update Process in Local Search

Shuffled frog leaping algorithm (SFLA) is meta-heuristic for solving complex optimization problems. It is one of promising optimistic methods which are based on swarm intelligence. SFLA combines the advantages of memetic algorithm and particle swarm optimization and has been widely used in engineering fields. In order to overcome the shortcomings of local search in the classic SFLA, a novel update method with convergence property is presented in this paper. On the basis of the proposed approach, the modified SFLA is presented afterwards. Experimental results show that the efficiency and convergence of the modified SFLA can be enhanced significantly.