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动态调整惯性权重的粒子群优化算法
引用本文:龙文,梁昔明,董淑华,阎纲.动态调整惯性权重的粒子群优化算法[J].计算机应用,2009,29(8).
作者姓名:龙文  梁昔明  董淑华  阎纲
作者单位:中南大学,信息科学与工程学院,长沙,410083
基金项目:国家自然科学基金,高等学校博士学科点专项科研基金 
摘    要:针对高维复杂优化问题,提出一种改进适应度函数和动态调整惯性权重的粒子群优化算法.首先考虑了搜索点的函数值及其变化率,并将该信息加入适应度函数.利用维惯性权重矩阵自适应动态调整惯性权重,较好地平衡了算法的全局探索和局部开发,并分析了惯性权重随种群多样性的变化关系.在算法后期计算每一维的收敛度,以一定的概率对收敛度最小的维进行变异,以加快算法的收敛速度.对高维测试函数的实验表明,算法提高了全局搜索能力.

关 键 词:改进适应度函数  惯性权重矩阵  粒子群优化  维变异

Particle swarm optimization with dynamic change of inertia weights
LONG Wen,LIANG Xi-ming,DONG Shu-hua,YAN Gang.Particle swarm optimization with dynamic change of inertia weights[J].journal of Computer Applications,2009,29(8).
Authors:LONG Wen  LIANG Xi-ming  DONG Shu-hua  YAN Gang
Affiliation:College of Information Science and Engineering;Central South University;Changsha Hunan 410083;China
Abstract:A novel particle swarm optimization (NPSO) with modified fitness function and dynamic change of inertia weights was proposed for solving complex high-dimensional optimization problems. In this algorithm, both the function value at the searching point and the function change rate at the point were combined into fitness function. This new approach could balance the local searching and the global searching by adopting inertia weight matrix to adaptively and dynamically adjust inertia weights. The convergence d...
Keywords:modified fitness function  inertia weight matrix  particle swarm optimization  dimension mutation
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