为了解决基本人工蜂群算法（ABC）早熟收敛、容易陷入局部最优、收敛精度不高等问题，提出一种混合改进的人工蜂群算法（RABC）。首先，为了平衡ABC的全局寻优能力，在初始化种群阶段引入了混沌算子和逆向学习算子；而后，为了提高局部寻优能力，在采蜜蜂的检索方程中引入了最优引导个体；最后，为了提高收敛精度和加快后期收敛速度，改进了侦察蜂的检索机制。为了验证RABC算法的收敛效果，通过在3个标准测试函数上的仿真实验，并与基本ABC算法的比较，发现RABC的收敛性能有显著提高。%In order to solve the problem of the basic Artificial Bee Colony (ABC)algorithm,such as the premature convergence,falling into local optimum easily,low convergence precision,put forward an Revised Artificial Bee Colony (RABC)algorithm.First,in order to balance the ABC global optimization ability,in the initialized population stage introduce the chaos operator and reverse learning operator. Then in order to improve the local optimization ability,in mining bee search equation introduce the best guide in the individual.Finally, in order to improve the convergence precision and speed up the convergence speed,improve the search mechanism of scout bees.In order to verify the convergence effect of RABC,through the simulation experiments on three standard test functions,and compared with the bas-ic ABC algorithm,found that the convergence of the RABC have improved significantly.
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