人工蜂群算法(ABC)是一种模拟蜜蜂群体寻找优良蜜源的群体智能优化算法.针对人工蜂群算法收敛速度较慢、探索能力较强而开发能力偏弱等问题,提出一种改进的蜂群算法.算法利用更多的较优蜜源位置的信息来引导采蜜蜂和跟随蜂的搜索行为.为了提高算法的全局收敛速度,通过基于混沌策略的方式生成初始化种群,并且在每一代侦察蜂阶段后对全部新蜜源进行反向再搜索.另外,每次蜜蜂邻域搜索之后,采用比较新旧蜜源的花蜜值(而非适应度值)的方法来更新蜜源位置.通过对14个标准测试函数进行仿真实验,结果表明所提出的算法能有效加快收敛速度,提高开发能力和解的精度. Artificial bee colony algorithm (ABC) is a swarm intelligent optimization algorithm that simulates the honey bee colony to find a good honey source.Aiming at the problems of artificial bee colony algorithm such as slow convergence rate, strong exploration ability and weak developing ability, an improved bee In order to improve the global convergence speed of the algorithm, the initial population is generated by the method based on chaos strategy, and in each stage of reconnaissance bee After that, the nectar value (rather than the fitness value) of the new and old nectar source is updated to be the nectar source position after each bee neighborhood search.Through 14 standard test functions Simulation results show that the proposed algorithm can effectively speed up the convergence rate and improve the accuracy of the development capability and solution.