Improved Hybridized Bat Algorithm for Global Numerical Optimization

Abstract

Swarm intelligence algorithms have been successfully applied to intractable optimization problems. Bat algorithm is one of the latest optimization metaheuristics and research about its capabilities and possible improvements is at the early stage. This algorithm has been recently hybridized with differential evolution and improved results were demonstrated on standard benchmark functions for unconstrained optimization. In this paper, in order to further enhance the performance of this hybridized algorithm, a modified bat-inspired differential evolution algorithm is proposed. The modifications include operators for mutation and crossover and modified elitism during selection of the best solution. It also involves the introduction of a new loudness and pulse rate functions in order to establish better balance between exploration and exploitation. We used the same five standard benchmark functions to verify the proposed algorithm. Experimental results show that in almost all cases, our proposed method outperforms the hybrid bat algorithm.