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粒子群优化算法的改进与性能分析

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摘要 粒子群优化算法(PSO)在众多的优化问题上表现出良好的性能,广泛应用于很多领域,但极易陷入局部最优解的困局.本文从提高收敛速度方面对PSO算法改进进行了研究,并通过仿真实验证明改进算法的可行性,一定程度上克服了PSO算法易于陷入局部最优解的缺点.
作者 于志奇
出处 《晋中学院学报》 2011年第3期20-22,共3页 Journal of Jinzhong University
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参考文献11

  • 1Kennedy J, Eberhart R C. A Discrete Binary Version of the Particle Swarm Algorithm[ M ].//Proceedings of the World Multi Cconference on Systems, Cybernetics and Informatics. Piscataway, Nagoya, Japan: IEEE Service Center, 1997 : 4104-4109. 被引量:1
  • 2Kennedy J, Eberhart R C. Particle swarm optimization[ M ].//Proceedings of IEEE International Conference on Neural networks, Perth ,Australia, 1995:1942-1948. 被引量:1
  • 3Fukuyama Y. Fundamentals of particle swarm techniques[ M ].//Modern Heuristic Optimization Techniques with Applications to Power Systems. IEEE Power Engineering Society, 2002: 45-51. 被引量:1
  • 4SHI Y, EBERHART R C. A modified particle swarm optimizer[ M ].//Proceedings of IEEE International Conference on Evolutionary Computation, 1998: 125-128. 被引量:1
  • 5张选平,杜玉平,秦国强,覃征.一种动态改变惯性权的自适应粒子群算法[J].西安交通大学学报,2005,39(10):1039-1042. 被引量:139
  • 6刘建华,樊晓平,瞿志华.一种惯性权重动态调整的新型粒子群算法[J].计算机工程与应用,2007,43(7):68-70. 被引量:49
  • 7SHI Y, EBERHART R C. Fuzzy adaptive particle swarm optimization[ M ].//Proceedings of the IEEE Conference on Evolutionaq Computation, 2001 : 101-106. 被引量:1
  • 8Clerc M. The swarm and the queen: Towards a deterministic and adaptive particle swarm optimization[ M ]//Proceedings of International Conference on Evolutionary Computation. Washington, USA, 1999:1951-1957. 被引量:1
  • 9任小波,杨忠秀.粒子群优化算法的改进[J].计算机工程,2010,36(7):205-207. 被引量:12
  • 10代军,李国,徐晨,陶艾.一种新的粒子群优化算法[J].计算机工程,2010,36(9):192-194. 被引量:10

二级参考文献25

  • 1曾建潮,崔志华.一种保证全局收敛的PSO算法[J].计算机研究与发展,2004,41(8):1333-1338. 被引量:160
  • 2窦全胜,周春光,马铭.粒子群优化的两种改进策略[J].计算机研究与发展,2005,42(5):897-904. 被引量:39
  • 3Kennedy J, Eberhart R C. Particle Swarm Optimization[C]// Proceedings of IEEE International Conference on Neural Networks. [S. l.]: IEEE Press, 1995: 1942-1948. 被引量:1
  • 4Eberhart R C, Kennedy J. A New Optimizer Using Particle Swarm[C]//Proceedings of the 6th International Symposium on Micro Machine and Human Science. Nagoya, Japan: IEEE Press, 1995: 39-43. 被引量:1
  • 5Eberhan R C, Shi Yuhui. Tracking and Optimizing Dynamic Systems with Particle Swarms[C]//Proceedings of the IEEE Congress on Evolutionary Computation. Seoul, Korea: [s. n.], 2001:94-100. 被引量:1
  • 6Kennedy J, Eberhart R C. Particle Swarm Optimization[C]//Proc. of IEEE International Conference on Neural Networks. Perth, Australia: IEEE Press, 1995. 被引量:1
  • 7Bergh F D, Engelbrecht A P. A Study of Particle Swarms Optimization Particle Yrajectories[J]. lnforlnation Sciences, 2006, 176(8): 937-971. 被引量:1
  • 8Xie Xiaofeng, Zhang Wenjun, Yang Zhilian. A Dissipative Particle Swarm Optimization[C]//Proc. of CEC'02. Honolulu, USA: [s. n.], 2002. 被引量:1
  • 9Chen Xin, Li Yangmin. A Modified PSO Structure Resulting in High Exploration Ability with Convergence Guaranteed[J]. IEEE Transactions on Systems, Man and Cybernetics, 2007, 37(5): 1271-1289. 被引量:1
  • 10Liang J J, Qin A K, Suganthan P N, et al. Comprehensive Learning Particle Swarm Optimizer for Global Optimization of Multimodal Functions[J]. IEEE Transactions on Evolutionary Computation, 2006, 10(3): 281-295. 被引量:1

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