An enhanced Bat algorithm with mutation operator for numerical optimization problems.

This article introduces a new variation of a known metaheuristic method for solving global optimization problems. The proposed algorithm is based on the Bat algorithm (BA), which is inspired by the micro-bat echolocation phenomenon, and addresses the problems of local-optima trapping using a special mutation operator that enhances the diversity of the standard BA, hence the name enhanced Bat algorithm (EBat). The design of EBat is introduced and its performance is evaluated against 24 of the standard benchmark functions, and compared to that of the standard BA, as well as to several well-established metaheuristic techniques. We also analyze the impact of different parameters on the EBat algorithm and determine the best combination of parameter values in the context of numerical optimization. The obtained results show that the new EBat method is indeed a promising addition to the arsenal of metaheuristic algorithms and can outperform several existing ones, including the original BA algorithm. [ABSTRACT FROM AUTHOR]