A novel animal migration algorithm for global numerical optimization

Abstract

Animal migration optimization (AMO) searches optimization solutions by migration process and updating process. In this paper, a novel migration process has been proposed to improve the exploration and exploitation ability of the animal migration optimization. Twenty-three typical benchmark test functions are applied to verify the effects of these improvements. The results show that the improved algorithm has faster convergence speed and higher convergence precision than the original animal migration optimization and other some intelligent optimization algorithms such as particle swarm optimization (PSO), cuckoo search (CS), firefly algorithm (FA), bat-inspired algorithm (BA) and artificial bee colony (ABC).