Research on the improved bat algorithms

Abstract

The bat algorithm (BA) was a kind of meta-heuristic algorithm that was simple and efficient in optimization, it had been widely applied in various fields. To improve the capability of the original BA, two methods used frequently in improvements were proposed in this paper: the gradient and sub-gradient methods, together with the Levy flights. Simulation experiments were carried out on the representatives of unimodal and multimodal benchmark functions. Results confirmed that not all traditional improvements were effective, some of the improved BA even work worse than the original one. However, the Levy flights could be considered a better replacement of randomness in applications.