全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

一种高效粒子群优化算法

, PP. 1158-1162

Keywords: 粒子群优化,局部搜索,学习算子,差分进化

Full-Text   Cite this paper   Add to My Lib

Abstract:

针对标准粒子群算法收敛速度慢和易出现早熟收敛等问题,提出一种高效粒子群优化算法.首先利用局部搜索算法的局部快速收敛性,对整个粒子群目前找到的最优位置进行局部搜索;然后,为了跳出局部最优,保持粒子的多样性,给出一个学习算子.该算法能增强算法的全局探索和局部开发能力.通过对10个标准测试函数的仿真实验并与其他算法相比较,结果表明了所提出的算法具有较快的收敛速度和很强的跳出局部最优的能力,优化性能得到显著提高.

References

[1]  Kennedy J, Eberhartr C. Particle swarm optimization [C]. Proc of IEEE Int Conf on Neural Networks.Perth: IEEE Piscataway, 1995: 1942-1948. [2] Jiao B, Lian Z G, Gu X S. A dynamic inertia weight particle swarm optimization algorithm [J]. Chaos Solitons $\&$ Fractals, 2008, 37(3): 698-705. [3] Jiang C W, Etorre B. A hybrid method of chaotic particle swarm optimization and linear interior for reactive power optimization [J]. Mathematics and Computers in Simulation, 2005, 68(1): 57-65. [4]Fan S F, Zahara E. Hybrid simplex search and particle swarm optimization for unconstratined optimization problems[J]. European Journal of Operational Research, 2007, 181(2):527–548. [5] 张顶学, 廖锐全. 一种基于种群速度的自适应粒子群算法[J]. 控制与决策, 2009, 23(7): 756-761.\\ (Zhang D X, Liao R Q. Adaptive particle swarm optimization algorithm based on population velocity[J]. Control and Decision, 2009, 23(7): 756-761.) [6] Hamzacebi C, Kutay F. Continuous functions minimization by dynamic random search technique[J]. Applied Mathematical Modelling. 2007, 31(10): 2189-198. [7] Hamzacebi C. Improving genetic algorithms’ performance by local search for continuous function optimization[J]. Applied Mathematics and Computation. 2008, 196(1): 309-317. [8] 潘正君, 康立山, 陈毓屏. 演化计算[M]. 北京: 清华大学出版社, 1998. [9] Yao X, Liu Y, Lin G. Evolutionary programming made faster[J].IEEE Transactions on Evolutionary Computation. 1999, 3 (2): 82-102. [10] Zhao X C. A perturbed particle swarm algorithm for numerical optimization[J]. Applied Soft Computing. 2010, 10(1): 119-124 . [11] Noman N, Iba H. Accelerating differential evolution using an adaptive local search[J]. IEEE Transactions on Evolutionary Computation. 2008, 12 (1): 107-125. [12] Lee C, Yao X. Evolutionary programming using mutations based on the levy probability distribution[J]. IEEE Transactions on Evolutionary Computation. 2004, 8 (1): 1-13. [13] Gimmler J, Stutzle T, Exner T. Hybrid particle swarm optimization: an examination of the influence of iterative improvement algorithms on performance[C]. Proceedings of the 5th International Workshop on Ant Colony Optimization and Swarm Intelligence. Heidelberg, Berlin, Germany: Springer-Verlag, 2006: 436-443. [14] Xu W B, Sun J. Adaptive parameter selection of quantum-behaved particle swarm optimization on global level[C]. Proceedings of International Conference of Intelligent Computing. Heidelberg, Berlin, Germany: Springer-Verlag, 2005: 420-428.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133