嵌入粒子群优化算法的混合人工蜂群算法  

Hybrid Artificial Bee Colony Algorithm Mixed with Particle Swarm Optimization Algorithm

作　　者：杨琳[1] 孔峰[1] 

机构地区：[1]广西工学院电子信息与控制工程系,广西柳州545006

出　　处：《自动化仪表》2013年第1期50-53,共4页Process Automation Instrumentation

基　　金：广西研究生教育创新计划基金资助项目(编号:2011105940811M01)

摘　　要：为了克服人工蜂群算法存在的早熟收敛、后期收敛速度变慢等缺点,提出了一种基于粒子群优化算法的混合人工蜂群算法(PABC)。对陷入局部极值的雇佣蜂,采用粒子群优化算法对其重新进行初始化。粒子群优化算法具有很强的全局搜索性能,能使陷入局部极值的雇佣蜂尽快摆脱局部约束。测试函数的计算结果表明,改进的人工蜂群算法大大提高了蜂群算法的寻优能力,在收敛速度和精度方面均优于基本蜂群算法。To overcome the shortcomings existing in artificial bee colony algorithm, e. g. , premature convergence and low rate in later convergence, the hybrid particle artificial bee colony （ PABC ） algorithm mixed with particle swarm optimization algorithm is proposed. To those employment bee getting into local extremum, the particle swarm optimization algorithm is adopted to re-initialize it. As the particle swarm optimization algorithm possesses good global search performance, the employment bee getting into local extremum can get rid of local restriction as quickly as possible. The calculation result of test function shows that the improved PABC enhances the optimization capability of bee colony algorithm, and the convergence rate and accuracy of algorithm are better than those of the basic bee colony algorithm.

关 键 词：人工蜂群算法 粒子群算法 搜索精度 优化能力 最优值 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]