关键词: 人工蜂群算法（ABC）, 十字交叉搜索, 局部学习, 数值优化, 邻域搜索

Abstract:  This paper presents an improved artificial bee colony algorithm to overcome the weaker search ability and imbalance in computing resource allocation. To enhance the neighborhood search of the elites and global best solution, two new strategies are first designed for the employed bees and onlooker bees, respectively. Then a local learning strategy is used to the onlooker bees chosen probably to speed up the convergence speed and enhance the global optimization ability. Finally, to balance the exploration and exploitation effectively, the crisscross search method is employed to enhance the performance of the onlooker bees and global best solution such that the diversity of the population is preserved and the premature convergence phenomenon is reduced. The proposed algorithm is compared with six excellent meta-heuristic algorithms on 10 classical benchmark functions and 30 CEC2014 benchmark functions with different dimensions. The experimental results show that the proposed algorithm is very competitive.

Key words: artificial bee colony (ABC), crisscross search, local learning, numerical optimization, neighborhood search