中文摘要：

针对经典人工蜂群（ABC）算法搜索策略存在搜索机制单一、群体全局搜索与局部搜索运算耦合性较高的问题,提出一种基于混合搜索的多种群人工蜂群（MPABC）算法。首先,将种群按照适应度值进行排序,得到一个有序队列,进而将其划分为随机子群、核心子群和平衡子群三类有序子群;其次,针对不同子群结合相应的个体选择机制与搜索策略,构建出不同的差异向量;最后,在群体的搜索过程中,通过三类子群实现对具有不同适应度函数值个体的有效控制,来增强群体全局搜索和局部搜索的平衡能力。通过对16个标准测试函数进行仿真实验并与具有可变搜索策略的人工蜂群（ABCVSS）算法、基于选择概率的改进人工蜂群（MABC）算法、基于粒子群策略的多精英人工蜂群（PS-MEABC）算法、基于符号函数的多搜索策略人工蜂群（MSSABC）算法和优化高维复杂函数的改进人工蜂群（IABC）算法共五种典型的蜂群算法进行了对比,实验结果显示MPABC具有较好的优化效果;与ABC算法相比,MPABC在求解高维（100维）复杂问题上的收敛速度提高了约23%,且求解精度更优。

英文摘要：

Aiming at the problems of Artificial Bee Colony （ABC） algorithm, which are the single search mechanism and the high coupling between global search and local search, a Multi-Population ABC （MPABC） algorithm based on hybrid search was proposed. Firstly, the population was sorted according to the fitness value to get an ordered queue, which was divided into three sorted subgroups including random subgroup, core subgroup and balanced subgroup. Secondly, different difference vectors were constructed according to the corresponding individual selection mechanism and search strategy to different subgroups. Finally, in the process of group search, the effective control of individuals with different fitness functions was realized through three subgroups, thus improving the balance ability of global search and local search. The simulation results based on 16 benchmark functions show that compared with ABC algorithm with Variable Search Strategy （ABCVSS）, Modified ABC algorithm based on selection probability （MABC）, Particle Swarm-inspired Multi-Elitist ABC （PS-MEABC） algorithm, Multi-Search Strategy of the ABC （MSSABC） and Improved ABC algorithm for optimizing high-dimensional complex functions （IABC）, MPABC achieves a better optimization effect; and on the solution of high dimensional （ 100 dimensions） problems, compared with ABC, MPABC has higher convergence speed which is increased by about 23% and better search accuracy.