Accelerating artificial bee colony algorithm by using an external archive

Abstract

Artificial bee colony (ABC) is a new optimization technique which has shown to be competitive with some wellknown evolutionary algorithms. However, ABC is good at exploration but poor at exploitation. Inspired by JADE (adaptive differential evolution with optional external archive), this paper proposes an improved ABC (IABC) algorithm with an external archive, which stores some best solutions during the search process to guide the search of ABC. Experiments are conducted on several benchmark functions. Computational results show that our approach achieves promising performance in terms of solution accuracy and convergence speed.