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基于动态邻居拓扑结构的PSO算法 被引量:1

Particle Swarm Optimization Algorithm Based on Dynamic Neighbor Topology Framework
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摘要 粒子群优化(PSO)算法在求解复杂的多峰问题时极易陷入局部最优解,通过分析种群多样性与局部最优解间的关系,提出一种基于动态邻居拓扑结构的粒子群算法。该算法在运行过程中,每间隔若干代,根据粒子间的距离更新每个粒子的邻居,该策略增加种群的多样性,进而提升粒子跳出局部最优解的能力。实验结果表明,该算法比其他PSO算法具有更好的性能。 Particle Swarm Optimization(PSO) algorithms may easily get trapped in a local optimum,when it solves complex multimodal problems,by analyzing the relationship between swarm diversity and local optima,this paper presents an improved particle swarm optimizer based on dynamic neighbor topology(DPSO for short).In DPSO,the neighbor of each particle is dynamically constructed at several iterations,which increases the swarm diversity and improves the ability to escape from local optima.In benchmark functions,the DPSO algorithm achieves better solutions than other PSO algorithms.
出处 《计算机工程》 CAS CSCD 北大核心 2011年第8期210-212,共3页 Computer Engineering
基金 山东省科技攻关计划基金资助项目(2009GG10001008) 广东省自然科学基金资助项目(9451806001002294) 深港创新圈基金资助项目(200810220137A) 贵州教育厅社科基金资助项目(0705204)
关键词 粒子群优化 动态邻居 种群多样性 函数评价 Particle Swarm Optimization(PSO); dynamic neighbor; swarm diversity; function evaluations;
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参考文献6

  • 1Kennedy J, Eberhart R C. Particle Swarm Optimization[C]//Proc. of IEEE International Conference on Neural Networks. New York, USA: [s. n.], 1995: 1942-1948.
  • 2Kennedy J, Mendes R. Neighborhood Topologies in Fullyinformed and Best-of-neighborhood Particle Swarms[J]. IEEE Transactions on Systems, Man and Cybernetics, 2006, 36(4): 156- 175.
  • 3阳春华,谷丽姗,桂卫华.自适应变异的粒子群优化算法[J].计算机工程,2008,34(16):188-190. 被引量:51
  • 4刘衍民,赵庆祯,隋常玲,邵增珍.一种基于动态邻居和变异因子的粒子群算法[J].控制与决策,2010,25(7):968-974. 被引量:32
  • 5Mendes R, Kennedy J, Neves J. The Fully Informed Particle Swarm: Simpler, Maybe Better[J]. IEEE Transactions on Evolutionary Computation, 2004, 7(8): 204-210.
  • 6van den Bergh F, Engelbrecht A P. A Cooperative Approach to Particle Swarm Optimization[J]. IEEE Transactions on Evolutionary Computation, 2004, 6(8): 225-239.

二级参考文献9

共引文献81

同被引文献22

  • 1张建忠,沈昱,周光涛,于丽,张晓光,杨伯君.粒子群优化算法在自适应偏振模色散补偿中的性能研究[J].光学学报,2006,26(1):1-6. 被引量:3
  • 2王雪飞,王芳,邱玉辉.一种具有动态拓扑结构的粒子群算法研究[J].计算机科学,2007,34(3):205-207. 被引量:16
  • 3Kennedy J, Eberhart R C.Particle swarm optimization[C]// Proceedings of IEEE International Conference on Neural Networks, 1995 : 1942-1948.
  • 4Kennedy J, Eberhart R C.A new optimizer using particleswarm theory[C]//Proceedings of the 6th Symposium on Micro Machine and Human Science, 1995:39-43.
  • 5Ghosh S,Kundu D, Suresh K,et al.On some properties of the lbest topology in particle swarm optimization[C]// Proceedings of the 5th International Conference on Hybrid Intelligent Systems, Shenyang, 2009 : 370-375.
  • 6Li Xiaodong.Niching without niching parameters: particle swarm optimization using a ring topology[J].IEEE Trans- actions on Evolutionary Computation,2010, 14(1): 150-169.
  • 7Marinakis Y, Marinaki M.Particle swarm optimization with expanding neighborhood topology for the permu- tation flowshop scheduling problem[J].Soft Computing, 2013,17: 1159-1173.
  • 8张丽萍.粒子群优化算法的理论及实践[D].杭州:浙江大学,2005.
  • 9Mendes R.Population topologies and their influence in particle swarm performance[D].University of Minho,2004.
  • 10Lu Haiyan, Chen Weiqi.Self-adaptive velocity particleswarm optimization for solving constrained optimization problems[J].Journal of Global Optimization, 2008,41: 427-445.

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