基于符号函数的多搜索策略人工蜂群算法

针对人工蜂群算法传统搜索策略在求解高维复杂函数时收敛速度较慢、容易陷入局部最优的缺陷，提出一种基于符号函数的多搜索策略人工蜂群算法。新算法借助符号函数将几种不同的搜索策略进行融合，在进化过程中充分发挥各搜索策略的优势，并基于目标函数值进行选择寻优，新算法能较好的平衡局部搜索能力和全局搜索能力。通过对16个基准函数的仿真实验及与其他改进算法的比较，表明了所提出的算法具有较快的收敛速度和较高的求解精度。

Multi-Search Strategy of Artificial Bee Colony Algorithm based on Symbolic Function

The traditional search strategy of artificial bee colony algorithm exist some disadvantages when solving complex functions with high dimensions, such as the convergence speed is not fast enough, easy to fall into local optimum. In order to solve this problem, multi-search strategy of artificial bee colony algorithm based on symbolic function is presented. In this algorithm, the new algorithm uses the symbolic function to fuse several different search strategies, give full play to the advantages of the search strategies during evolution, and select the best solution based on the objective function value. The new algorithm can balance the ability of local and global search. Experiments were conducted on a set of 16 benchmark functions, and the results demonstrate that the new algorithm has fast convergence and high accuracy than several other ABC-based algorithms.