针对人工蜂群(ABC)算法开采能力差的问题，提出一种逐步加强开采能力的改进ABC算法.在雇佣蜂阶段，向局部最优解学习，并逐步增大局部最优解的比率；在观察蜂阶段，向局部最优解和全局最优解学习，并逐步增大全局最优解的比率，从而较好地平衡算法的勘探与开采能力.在CEC2014等36个函数上进行实验，结果表明，改进ABC算法的性能明显优于ABC-NS和CoDE等算法.
Concerning the issue that solution search equation of artificial bee colony (ABC) algorithm does well in exploration but badly in exploitation, a new method is proposed to gradually enhance the exploitation ability of ABC algorithm. In the proposed algorithm, employed bees learn from the local best individuals and proportion of the local best individuals is gradually enhanced. Onlooker bees learn from the local best and global best individuals and proportion of the global best individual is gradually enhanced. A forementioned measures can effectively balance the exploration and exploitation ability of ABC algorithm. The experiments are conducted on 36 benchmark functions, including CEC2014 problems. The performance of the proposed algorithm outperforms those of ABC with neighborhood search (ABC-NS) and composite DE (CoDE) algorithms significantly.
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