摘要
针对人工蜂群算法传统搜索策略在求解高维复杂函数时存在收敛速度较慢、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.该算法将几种不同的搜索策略借助符号函数进行融合,在进化过程中充分发挥各搜索策略的优势,可以较好地平衡算法的局部搜索能力和全局搜索能力,同时基于目标函数值进行选择寻优.通过对16个基准函数进行的仿真实验以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.
The traditional search strategy of the artificial bee colony（ABC） algorithm exists some disadvantages when solving complex functions with high dimensions, such as that the convergence speed is not fast enough, easy to fall into local optimum. In order to solve these issues, the multi-search strategy of the artificial bee colony（MSSABC） algorithm based on the symbolic function is presented. The new algorithm uses the symbolic function to fuse several different search strategies,makes full use of the advantages of the different search strategies during evolution to balance the local search ability and the global search ability, and selects the best solution based on the objective function value. Experiments are conducted on a set of 16 benchmark functions, and the results show that the proposed algorithm has fast convergence and high accuracy than several other ABC-based algorithms.
出处
《控制与决策》
EI
CSCD
北大核心
2016年第11期2037-2044,共8页
Control and Decision
基金
国家自然科学基金项目(71503132)
江苏省高校自然科学研究项目(14KJD110005
14KJB110017)
关键词
人工蜂群算法
符号函数
搜索策略
artificial bee colony algorithm
symbolic function
search strategy
