摘要
针对人工蜂群算法存在的计算精度不高、收敛速度较慢的缺点,提出一种多搜索策略协同进化的人工蜂群算法.所提出的算法在引领蜂和跟随蜂进行邻域搜索时,动态调整搜索的维数以提高搜索效率,并结合人工蜂群算法不同搜索策略的特点,使其协同进化,以平衡算法的局部搜索能力和全局搜索能力.14个基准函数的仿真实验结果表明,所提出的算法能有效改善寻优性能,增强摆脱局部最优的能力.与其他一些改进的人工蜂群算法相比,具有较快的收敛速度和较高的求解精度.
An artificial bee colony（ABC） algorithm with multi-search strategy cooperative evolutionary is presented in order to overcome the drawbacks of low computational accuracy and slow convergence of the artificial bee colony algorithm. In this algorithm, the dimensions of the search are dynamically adjusted when the employed bees and the onlooker bees search around the neighborhood to improve the search efficiency. The characteristics of different search strategies are combined to cooperative evolution so as to improve the local search ability and the global search ability. Experiments are conducted on a set of 14 benchmark functions, and the results demonstrate that the proposed algorithm can improve optimizing performance and avoid getting struck at local optima effectively. Compared with several other ABC-based algorithms, it has fast convergence and high accuracy.
出处
《控制与决策》
EI
CSCD
北大核心
2018年第2期235-241,共7页
Control and Decision
基金
国家自然科学基金项目(11601234)
江苏省自然科学基金项目(BK20160571)
江苏省青蓝工程基金项目
关键词
人工蜂群算法
搜索策略
协同进化
artificial bee colony algorithm
search strategy
cooperative evolution
