Multi-strategy and Dimension Perturbation Ensemble of Artificial Bee Colony

Abstract

Ensemble learning is a popular machine learning technique, which employs multiple learning methods to obtain better performance than any single constituent method. Recently, ensemble learning was successfully used in several bio-inspired optimization algorithms to achieve good performance. Artificial bee colony (ABC) is an efficient optimization technique inspired by the social behavior of bees. To enhance the optimization ability of ABC, this paper proposes a new ABC variant based on ensemble learning. The proposed approach is called multi-strategy and dimension perturbation ensemble of ABC (MPEABC), in which multiple distinct solution search strategies are used to balance the exploration and exploitation. To accelerate the search, each solution search strategy is assigned an independent probability to control the frequency of dimension perturbation. To avoid manually setting the probability, an adaptive method is used to dynamically adjust its value. Experimental verifications are conducted on a set of well-known benchmark functions. Results show that MPEABC achieves better solutions than the standard ABC, multi-strategy ensemble ABC (MEABC) and several improved ABC variants.