Large-scale Global Optimization based on ABC Algorithm with Gbest-guided Strategy and Opposite-based Learning Technique

A novel approach for better solving large scale global optimization (LSGO) problem, which incorporates an enhanced artificial bee colony (ABC) algorithm and adaptive neighborhood search technique, is presented in this paper. In the enhanced ABC algorithm, the gbest-guided strategy is employed to better exploit a local area, while an opposite-based learning technique is simultaneously used to enhance its exploration capability. In addition, a self-adaptive neighborhood search strategy with Gaussian and Cauchy operators is used to balance the local and global search process. Experimental results on 20 benchmark functions of CEC2010 can not only verify the effectiveness and efficiency of this method, but also indicate the enhanced ABC algorithm with gbest-guided strategy and opposite-based learning technique can achieve comparable or better solution quality with respect to some other approaches in LSGO.