Bat algorithm with the characteristics of Lévy flights

The basic bat algorithm(BA) in the past research studies reveal deficiencies as apt to be premature and low precision of convergence.This paper first analyzed the optimization mechanism and deficiency of bat algorithm(BA),and then considering the Levy flight behaviors of bats can simulate predatory more realistically,the study proposed substituting for the speed and location updating pattern of former algorithm.The proposed algorithm fully explored the trait of uneven random walks,so that clusters of short steps were connected by rare long steps,to avoid being trapped in local optimal solution.Simulation results for benchmark functions show that the proposed algorithm improved the global optimization ability remarkably and outperformed the basic BA and particle swarm optimization(PSO) in accuracy and convergence property.Therefore,the proposed algorithm is an effective tool for solving the optimization of complex functions.