A Self-adaptive Artificial Bee Colony Algorithm with Symmetry Initialization

Abstract

The Artificial Bee Colony (ABC) algorithm is an optimization algorithm inspired by the foraging behavior of bee swarms. Similar to some evolutionary algorithms, there is a main limitation in ABC, i.e., in many problems, ABC is good at exploration but poor at exploitation. Thus, in order to overcome this limitation and improve the performance of ABC when dealing with various kinds of optimization problems, we proposed a self-adaptive artificial bee colony algorithm with symmetry initialization (SABC-SI). In our SABC-SI algorithm, a novel population initialization method based on half space and symmetry is designed, and such method can increase the diversity of initial solutions. Besides, a self- adaptive search mechanism and several new Candidate Solution Generating Strategies (CSGSes) have also been developed. Consequently, the evolutionary strategies can be selected dynamically according to their search performance. Moreover, the selection operator is improved by eliminating some of the poor solutions and making good use of the two best solutions in both the current and previous generations. The novel algorithm was tested on 25 different benchmark functions. The experimental results show that SABC-SI outperforms several state-of-the-art algorithms, which indicates that it has great potential to be applied to a wide range of optimization problems.