Artificial bee colony algorithm based on adaptive neighborhood search and Gaussian perturbation

Abstract Artificial bee colony (ABC) is a type of popular swarm intelligence optimization algorithm. It is widely concerned because of its easy implementation, few parameters and strong global search ability. However, there are some limitations for ABC, such as weak exploitation ability and slow convergence. In this paper, a novel ABC with adaptive neighborhood search and Gaussian perturbation (called ABCNG) is proposed to overcome these shortcomings. Firstly, an adaptive method is used to dynamically adjust the neighborhood size. Then, a modified global best solution guided search strategy is constructed based on the neighborhood structure. Finally, a new Gaussian perturbation with evolutionary rate is designed to evolve the unchanged solutions at each iteration. Performance of ABCNG is tested on two benchmark sets and compared with some excellent ABC variants. Results show ABCNG is more competitive than six other ABCs.