Selection Mechanism in Artificial Bee Colony Algorithm: A Comparative Study on Numerical Benchmark Problems

Abstract

Artificial bee colony (ABC) is a very effective and efficient swarm-based intelligence optimization algorithm, which has attracted a lot of attention in the community of evolutionary algorithms. Until now, many different variants of ABC have been proposed, and most of them are concentrated on improvement of the solution search equation. However, few works have been focused on the selection mechanism in the onlooker bee phase which is an important component of ABC. In this paper, hence, we present a comparative study on the selection mechanism to investigate its effect on the performance of ABC. Six different selection mechanisms are included in the comparison, and 21 well-known benchmark problems are used in the experiments. Results show that the fitness rank-based mechanisms perform better.