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基于变惯性权重及动态邻域的改进PSO算法 被引量：14

Improved PSO Algorithm Based on Variety Inertia Weight and Dynamic Neighborhood
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摘要 分析并验证基于变惯性权重的粒子群优化(PSO)在粒子寻优过程中的有效性,论述类无标度网的特殊拓扑性质。将有向动态类无标度网作为粒子寻优邻域,提出一种基于变惯性权重及动态邻域的改进PSO算法。实验结果证明,与传统PSO算法相比,改进算法的寻优效果较好,可在一定程度上避免陷入局部最优。 This paper analyzes and verifies the effectiveness of Particle Swarm Optimization（PSO） based on variety inertia weight in the particle optimization process,and discusses the special topological properties of scale-free like network.It uses the dynamic scale-free like network as the particle’s optimization neighborhood.It proposes an improved PSO algorithm based on variety inertia weight and dynamic neighborhood.Experimental results show that the improved algorithm performs better than the traditional PSO and may avoid falling into the local optimum instead.
作者 姚灿中 杨建梅
机构地区 华南理工大学经济与贸易学院 华南理工大学工商管理学院
出处 《计算机工程》 CAS CSCD 北大核心 2011年第21期20-22,共3页 Computer Engineering
基金 国家自然科学基金资助项目(71173076 71103044) 教育部人文社会科学研究青年基金资助项目(11YJCZH211 08JC790023) 中央高校基本科研业务费专项基金资助项目(2011ZB0011) 广东省哲学社会科学"十一五"规划基金资助项目(07GO02)
关键词 粒子群优化 类无标度网 惯性权重 度分布 邻域拓扑 Particle Swarm Optimization（PSO） scale-free like network inertia weight degree distribution neighborhood topology
分类号 TP301 [自动化与计算机技术—计算机系统结构]
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参考文献7

  • 1Eberhart R C, Kennedy J. A New Optimizer Using Particle Swarm Theory[C]//Proc. of the 6th International Symposium on Micro Machine and Human Science. Nagoya, Japan: [s. n.], 1995.
  • 2Kennedy J. Small Worlds and Mega-minds: Effects of Neigh- borhood Topology on Particle Swarm Performance[C]//Proc. of Congress on Evolhtionary Computation. Washington D. C., USA: Is. n.], 1999.
  • 3温雯,郝志峰.一种基于动态拓扑结构的PSO改进算法[J].计算机工程与应用,2005,41(34):82-85. 被引量：13
  • 4姚灿中,杨建梅.基于网络邻域拓扑的粒子群优化算法[J].计算机工程,2010,36(19):18-20. 被引量：5
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二级参考文献15

  • 1温雯,郝志峰.一种基于动态拓扑结构的PSO改进算法[J].计算机工程与应用,2005,41(34):82-85. 被引量：13
  • 2[1]Eberhart R,Kennedy J.A new optimizer using particle swarm theory[C].In:Proceedings of the Sixth International Symposium on Micro Machine and Human Science,1995-10:39～43
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  • 7[6]Xiaodong Li.Adaptively Choosing Neighbourhood Bests Using pecies in a Particle Swarm Optimizer for Multimodal Function Optimization[C].In:proceedings of Genetic and Evolutionary Computation Conference,2004-06:105～116
  • 8[7]Thiemo Krink,Morten Lovbjerg.The LifeCycle Model:Combining Particle Swarm Optimisation,Genetic Algorithms and HillClimebers[C].In:Proceedings of the seventh international conference of Parallel Problem Solving from Nature,2002-09:621～630
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  • 10[9]Marco Dorigo,Luca Maria Gambardella.Ant colonies for the traveling salesman problem[J].Biosystems,1997;43(2) :73～81

共引文献16

同被引文献101

引证文献14

二级引证文献254

计算机工程
2011年 第21期

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