Chaotic bat algorithm

• Four chaotic bat algorithms (CBAs) are proposed here for global optimization.
• Thirteen different chaotic maps are utilized to replace with the main parameters of the CBAs.
• Comparing the new chaotic algorithms with the standard BA demonstrates superiority of the CBAs for the benchmark functions.

Bat algorithm (BA) is a recent metaheuristic optimization algorithm proposed by Yang. In the present study, we have introduced chaos into BA so as to increase its global search mobility for robust global optimization. Detailed studies have been carried out on benchmark problems with different chaotic maps. Here, four different variants of chaotic BA are introduced and thirteen different chaotic maps are utilized for validating each of these four variants. The results show that some variants of chaotic BAs can clearly outperform the standard BA for these benchmarks.