Enhancing Artificial Bee Colony Algorithm with Directional Information

Abstract

Artificial bee colony (ABC) algorithm is a swarm intelligence based optimization technique, which has attracted wide attention from different research fields. In the basic ABC, however, the same solution search equation is used in both of the employed bee phase and onlooker bee phase, which performs well in exploration but poorly in exploitation. To address this concerning defect, in this paper, we propose an improved ABC variant by designing a mechanism of utilizing directional information. In this mechanism, we first construct a pool of differential vectors in the employed bee phase, and then utilize a differential vector randomly selected from the pool as directional information to guide search in the onlooker bee phase. Furthermore, we propose two novel solution search equations based on the current best solution and some good solutions with the aim of balancing exploration and exploitation. Experiments are conducted on a set of 22 well-known benchmark functions, and the results demonstrate that our proposed approach shows promising performance.