Fractional Lévy flight bat algorithm for global optimisation

Abstract

A well-known metaheuristic is the bat algorithm (BA), which consists of an iterative learning process inspired by bats echolocation behaviour in searching for prays. Basically, the BA uses a predefined number of bats that collectively move on the search space to find the global optimum. This article proposes the fractional Lévy flight bat algorithm (FLFBA), which is an improved version of the classical BA. In the FLFBA the velocity is updated through fractional calculus and a local search procedure that uses a random walk based on Lévy distribution. Such modifications enhance the ability of the algorithm to escape from local optimal values. The FLFBA has been tested using several well-known benchmark functions and its convergence is also compared with other evolutionary algorithms from the state-of-the-art. The results indicate that the FLFBA provided in several cases better performance in comparison to the selected evolutionary algorithms