基于动态P混沌与动态非线性的PSO算法  

Particle Swarm Optimization Algorithm Based on Dynamic P Chaos and Dynamic Nonlinear

作　　者：刘怀亮[1] 许若宁[2] 高鹰[1] 苏瑞娟[1] 

机构地区：[1]广州大学计算机科学与教育软件学院,广州510006 [2]广州大学数学与信息科学学院,广州510006

出　　处：《计算机工程》2010年第10期223-225,共3页Computer Engineering

基　　金：广东省自然科学基金资助项目(8451009101001040)

摘　　要：为解决粒子群优化算法易于陷入局部最优问题,提出2种方法并行改进惯性权重。对比平均值差的粒子,用所设计的动态P混沌映射公式调整惯性权重,在复杂多变的环境中逐步摆脱局部最优,动态寻找全局最优值。对好于或等于整体适应度平均值的粒子,用所提出的动态非线性方程调整惯性权重,在保存有利条件的基础上逐步向全局最优处收敛。2种方法前后相辅相成、动态协作。实验结果证实,该算法在不同情况下都超越了同类改进算法。To solve the problem of converging to local optima,two methods are introduced to modify the inertia weight in parallel.When particles’ fitness values are worse than the average,the dynamic P chaotic map formula is devised to modify the inertia weight,which can make particles break away from local best and search global best dynamically.The introduced dynamic nonlinear equation is used to modify the inertia weight,which can make particles retain favorable conditions and converge to global best continually.Two methods coordinate dynamically.Experimental results demonstrate that the introduced algorithm outperforms other improved Particle Swarm Optimization（PSO） algorithms on many well-known benchmark problems.

关 键 词：粒子群优化 惯性权重 动态P混沌映射 动态非线性方程 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]