Artificial Bee Group Colony Algorithm for Numerical Function Optimization

Abstract

In this paper, we propose an artificial bee group colony algorithm for numerical function optimization, based on the thoughts of group competition and similar property characteristic. Our algorithm contains three optimization strategies including grouping strategy, similar property strategy and competition strategy, which could not only ensure the algorithm finds better solutions stably, but also induce the algorithm to maintain solution diversification. Moreover, the similar property strategy could produce efficient exploring to find better solutions with skipping optimization. We evaluated the performance of our proposed algorithm on some standard numerical benchmark functions. The results demonstrate that our algorithm is able to yield higher quality solutions with faster convergence than either the original ABC or some other authoritative swarm intelligent algorithms.