基于极值优化策略的改进的人工蜂群算法  

Improved Artificial Bee Colony Algorithms Based on Extremal Optimization Strategy

作　　者：葛宇[1] 梁静[2] 王学平[3] 

机构地区：[1]四川师范大学基础教学学院成都610068 [2]成都工业学院网络中心成都610031 [3]四川师范大学数学与软件科学学院成都610068

出　　处：《计算机科学》2013年第6期247-251,共5页Computer Science

基　　金：四川省教育厅项目(12ZB112)资助

摘　　要：为提高人工蜂群算法在求解优化问题中的性能,结合极值优化策略提出一种改进的人工蜂群算法。改进算法基于极值优化策略高效率的寻优机制重新设计了原算法中跟随蜂的局部搜索方案,并具体给出了新方案的组元变异算子和最差组元判定规则。通过对优化问题中8个典型测试函数的仿真实验表明,与基本人工蜂群算法和已有的典型改进算法相比,改进算法在寻优精度和收敛速度上均有明显提高,在优化问题求解中体现出较强的寻优能力。In order to enhance the performance of artificial bee colony algorithm in solving optimization problems, this paper proposed an improved artificial bee colony algorithra. The improved algorithm redesigns local search scheme of onlook bees based on evolution method of extremal optimization strategy, and implements operators of component muta- tions, formulates rules of worst component judgment. The simulation results of eight typical functions of optimization problems show that the proposed algorithm can attain significant improvement on accuracy and convergent speed, has a better solution capability, compared with the basic artificial bee colony algorithm and known improved algorithm.

关 键 词：人工蜂群算法 极值优化策略 搜索方案 局部搜索 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]