Diversity analysis of population in shuffled frog leaping algorithm

Abstract

The diversity of population is an important indicator for measuring optimal performance of swarm intelligence algorithms. The effect of three operators of Shuffled Frog Leaping Algorithm (SFLA) on the diversity of population and the average optimization results were analyzed in this paper by means of the simulation experiments. The results show that removing the global extreme learning operator will not only maintain the higher diversity of population, but also improve the operating speed and the optimization precision of the algorithm. © 2013 Springer-Verlag Berlin Heidelberg.