Enhancing the modified artificial bee colony algorithm with neighborhood search

As a relatively new optimization technique, in recent years, artificial bee colony (ABC) algorithm has attracted much attention for its good performance. However, its performance has also been challenged in solving complex optimization problems. This insufficiency is mainly caused by its solution search equation, which does well in exploration but badly in exploitation. Inspired by the concept of neighborhood search, in this paper, we introduce a global neighborhood search operator into ABC for balancing its explorative and exploitative capabilities. Extensive experiments are conducted on 22 benchmark functions, and six different algorithms are included in the comparison studies, including four ABC variants and two related evolutionary algorithms. The compared results demonstrate that in most cases our approach is able to provide better performance in terms of solution accuracy and convergence speed.