Abstract: Artificial bee colony algorithm has the disadvantages that it is easy to fall into local optimum and has slow convergence speed, this paper proposes an improved artificial bee colony algorithm based on local search strategy. In this algorithm, follow bees adopt chaotic local search strategy based on the optimal solutions, scout bees use adaptive detection strategy based on the optimal solutions, and its local search range is narrowed with the increase of iterations, so as to improve the local search ability of artificial bee colony algorithm and effectively avoid falling into local optimal. The simulation experiments of six test functions show that, compared with the traditional artificial bee colony algorithms, the improved one can obviously increase the solution accuracy and convergence speed.

Key words: artificial bee colony algorithm, local search, current optimal, chaotic, adaptive detection

摘要： 针对人工蜂群算法在函数优化问题求解过程中容易陷入局部最优，收敛速度慢的缺点，提出了一种基于改进局部搜索策略的人工蜂群算法。该算法中跟随蜂采用基于当前最优解的混沌局部搜索策略，侦查蜂采用基于当前最优解的自适应侦查策略，并使其局部搜索范围随着迭代次数的增加逐渐减小，从而提高了人工蜂群算法的局部搜索能力，有效地避免了其陷入局部最优。6个测试函数的仿真实验结果表明，与传统的人工蜂群算法相比，改进后算法的求解精度和收敛速度明显提升。

关键词: 人工蜂群算法, 局部搜索, 当前最优解, 混沌, 自适应侦查