Abstract: Considering that the existing Artificial Bee Colony（ABC） can not pay simultaneous attention to evolution speed and solution quality, a Nelder-Mead Simplex Method based Improved Artificial Bee Colony（NMSM-IABC） is proposed in this paper. In the process of iteration, the algorithm periodically gets the best individual vertex from NMSM operator and migrates to ABC, or obtains the optimal nectar source information from ABC and migrates to NMSM. ABC can improve its local exploiting capability applying NMSM, and NMSM can get away from local minimum by ABC. The algorithm proposed can achieve cooperative search of the ABC and NMSM. Furthermore, in order to enhance the ability or increase the convergence speed of NMSM-IABC, an improved search scheme of onlooker bee is proposed. And the sensitivity analysis of the key parameter is conducted. Finally, numerical experiments and comparisons on six benchmark functions indicate that the proposed algorithm can avoid the local minimum and enhance the global search ability and convergence speed, and is an effective cooperative search algorithm.

Key words: Artificial Bee Colony（ABC）, Nelder-Mead Simplex Method（NM-SM）, cooperative search, sensitivity analysis, global search

摘要： 针对现有的人工蜂群算法（Artificial Bee Colony，ABC）在进化速度和求解质量方面难以兼顾的缺点，提出一种基于Nelder-mead单纯形法的改进人工蜂群算法（Nelder-Mead Simplex Method based Improved Artificial Bee Colony，NMSM-IABC）。在迭代过程中，该算法周期性地将单纯形算子得到的最优个体迁移到人工蜂群算法的蜂群中，或将蜂群中的最优蜜源信息迁移到Nelder-mead单纯形算法中。旨在ABC借助NM-SM提高局部搜索能力，NM-SM借助ABC跳出局部最优点，达到两者协同搜索。再者，为了进一步加快收敛速度，在ABC中采用一种改进的跟随蜂搜索策略，并对产生侦察蜂的关键参数进行灵敏度分析。最后，通过6个典型的多维测试函数对算法进行仿真测试。结果表明：提出的算法有效地避免了陷入局部最优，提高全局搜索能力和搜索精度，有较快的收敛速度，是一种较好的协同搜索算法。

关键词: 人工蜂群算法, Nelder-mead单纯形法, 协同搜索, 灵敏度分析, 全局搜索