| 基于动态惯性因子的PSO算法的研究 |
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| 引用本文： | 朱小六,熊伟丽,徐保国.基于动态惯性因子的PSO算法的研究[J].计算机仿真,2007,24(5):154-157. |
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| 作者姓名： | 朱小六 熊伟丽 徐保国 |
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| 作者单位： | 1. 江南大学通信与控制工程学院,江苏,无锡,214122
2. 江南大学控制科学与工程研究中心,江苏,无锡,214122 |
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| 摘 要： | 标准粒子群算法是一种有效的寻找函数极值的演化计算方法,它简便易行,收敛速度快.但算法也存在收敛精度不高,易陷入局部极值点的缺点.针对这些缺点,在原有算法的基础上,提出一种动态改变惯性因子的粒子群优化算法(DCWPSO),使得粒子在迭代过程中惯性因子随粒子进化度和聚合度的变化而改变.文中通过对测试函数的仿真实验,并与自适应改变惯性因子的粒子群算法(ACWPSO)以及标准粒子群算法比较,其结果表明这种改进的粒子群算法在性能上明显优于这两种粒子群算法.
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| 关 键 词： | 优化算法 动态惯性因子 进化度 聚合度 动态改变 惯性因子 算法比较 研究 Weight Dynamic Based Optimization Algorithm Swarm 性能 改进 结果 粒子群优化算法 自适应 仿真实验 测试函数 变化 聚合度 迭代过程 极值点 |
| 文章编号： | 1006-9348（2007）05-0154-04 |
| 修稿时间： | 2006-04-062006-04-13 |
A Particle Swarm Optimization Algorithm Based on Dynamic Intertia Weight |
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| ZHU Xiao-liu,Xiong Wei-li,Xu Bao-guo.A Particle Swarm Optimization Algorithm Based on Dynamic Intertia Weight[J].Computer Simulation,2007,24(5):154-157. |
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| Authors: | ZHU Xiao-liu Xiong Wei-li Xu Bao-guo |
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| Affiliation: | 1. School of Communication and Control Engineering , Southern Yangtze University, Wuxi Jiangsu 214122, China; 2. Control Science and Engineering Research Center , Southern Yangtze University,Wuxi Jiangsu 214122, China |
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| Abstract: | The normal PSO algorithm is a validated evolutionary computation way of searching the extremum of function , which is simple in application and quick in convergence , but low in precision and easy in premature convergence. Because of the limitation , a dynamically changing inertia weight PSO algorithm is proposed based on the normal PSO algorithm . The inertia weight is changed in every loop according to the swarm evolution degree and aggregation degree factor . Compared with ACWPSO and the normal PSO , the optimization results of testing function show that the performance of the DCWPSO algorithm is more excellent. |
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| Keywords: | Optimization arithmetic Dynamical intera weight factor Evolution degree Aggregation degree |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
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