关键词: 人工蜂群算法, 近似梯度, 局部搜索, 合作与共享

Abstract: To solve the problems of inferior local search ability, slow convergence and easily trapping into the local optimization existing in the artificial bee colony algorithm, this paper proposes a gradient search strategy based on population by introducing gradient information and using central difference schemes for gradient approximation processing. The novel search strategy used by employed bees improves the local search ability while the scout bees still employ global random searching strategy to avoid premature phenomenon led by fast convergence in solving multimodal problems. Simulation results in six standard test functions show that the proposed searching mechanism gives a good balance between local solution and global exploration and improves the optimization ability of different kinds of optimization problems.

Key words: artificial bee colony algorithm, approximate gradient, local search, cooperation and sharing