基于分治策略的改进人工蜂群算法

人工蜂群(ABC)算法存在着收敛速度不够快、易陷入局部最优的缺陷。针对这一问题，提出一种改进的人工蜂群(DCABC)算法。应用反学习的初始化方法产生初始解，引入分治策略对蜜源进行优化，在采蜜蜂发布更新的蜜源信息后，跟随蜂选择最优蜜源，并采用分治策略进行迭代优化。通过对经典测试函数的反复实验及与其他算法的比较，表明了所提出的算法具有良好的加速收敛效果，提高了全局搜索能力与效率。

Modified artificial bee colony algorithm based on divide-and-conquer strategy

As a kind of swarm optimization algorithm with good performance, the artificial bee colony (ABC) algorithm is presented in recent years. However, it exist some disadvantages, such as the convergence speed is not fast enough, easy to fall into local optimum and etc. In order to solve this problem, an improved algorithm called DCABC is presented. In this algorithm, the opposition-based learning method is employed when producing the initial population, the divide-and-conquer strategy is adopted to greed update food resources. After employed bees releasing updated food source information, onlookers choose optimal resource based on the divide-and-conquer strategy. Experiments are conducted on a set of 6 benchmark functions, and the results show that DCABC has better performance than several other ABC-based algorithms, especially on the accelerating convergence and the global search ability and efficiency.