Baby Search Algorithm

Abstract

In this paper, we propose a novel meta-heuristic algorithm, called Baby Search Algorithm (BSA), for solving complex optimization problems. BSA mimics the behavior of babies who have strong curiosity about external things and can actively look for things of interest. Generally speaking, babies search behaviors are divided into two types, one is to find another interesting thing, which is similar to exploration in the optimization problem; the other is to approach the most interesting thing at present, which is similar to exploitation. In order to balance exploration and exploitation, we utilize two probability thresholds to control it. In addition, we further combine Levy flight and mutation operations in BSA, which can make the babies search get closer to the nature of searching behavior. We validate the proposed algorithm against test functions and then compare its performance with those of twelve state-of-the-art algorithms. Finally, we discuss the implication of the results and suggestion for further research.