A multi-strategy artificial bee colony algorithm with neighborhood search

Abstract

As an effective swarm intelligence based optimization technique, artificial bee colony (ABC) algorithm has become popular in recent years. However, its performance is still not satisfied in solving some complex optimization problems. The main reason is that both of the employed bee phase and onlooker bee phase use the same solution search equation to generate new candidate solutions, and the solution search equation is good at exploration but poor at exploitation. To solve this problem, in this paper, we propose a multi-strategy artificial bee colony algorithm with neighborhood search (MSABC-NS). In MSABC-NS, a multi-strategy mechanism is designed to use two different solution search equations, and a neighborhood search mechanism is introduced to make full use of good solutions. Experiments are conducted on 22 widely used benchmark functions, and three different ABC variants are included in the comparison. The results show that our approach can achieve better performance on most of the benchmark functions.