带可变随机函数和变异算子的粒子群优化算法

周晓君; 阳春华; 桂卫华; 董天雪

doi:10.3724/SP.J.1004.2014.01339

带可变随机函数和变异算子的粒子群优化算法

doi: 10.3724/SP.J.1004.2014.01339

1.
中南大学信息科学与工程学院长沙 410083;
2.
澳大利亚联邦大学科学与信息技术及工程学院巴拉瑞特 3353

基金项目:

Supported by National Natural Science Found for Distinguished Young Scholars of China (61025015), the Foundation for Innovative Research Groups of National Natural Science Foundation of China (61321003) and the China Scholarship Council

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出版历程
- 收稿日期: 2011-05-30
- 修回日期: 2013-11-21
- 刊出日期: 2014-07-20

A Particle Swarm Optimization Algorithm with Variable Random Functions and Mutation

1.
School of Information Science and Engineering, Central South University, Changsha 410083, China;
2.
School of Science, Infor-mation Technology and Engineering, University of Ballarat, Victoria 3353, Australia

Funds:

摘要

摘要: 标准粒子群优化算法的收敛分析表明，改变随机函数、个体历史最优，群体全局最优，有助于提高该算法的性能。为此，本文提出了一种带可变随机函数和变异算子的粒子群优化算法，即通过改变速度更新方程中的随机函数分布来调节粒子在迭代过程中飞向个体历史最优和群体全局最优的比重，通过对个体历史最优和群体全局最优进行变异来增强种群的搜索能力。实验结果证实了该算法的有效性。
- 粒子群优化算法 /
- 随机函数 /
- 变异 /
- 种群密度
Abstract: The convergence analysis of the standard particle swarm optimization (PSO) has shown that the changing of random functions, personal best and group best has the potential to improve the performance of the PSO. In this paper, a novel strategy with variable random functions and polynomial mutation is introduced into the PSO, which is called particle swarm optimization algorithm with variable random functions and mutation (PSO-RM). Random functions are adjusted with the density of the population so as to manipulate the weight of cognition part and social part. Mutation is executed on both personal best particle and group best particle to explore new areas. Experiment results have demonstrated the effectiveness of the strategy.
- Particle swarm optimization /
- random functions /
- mutation /
- population density

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参考文献(29)

[1]	Banks A, Vincent J, Anyakoha C. A review of particle swarm optimization Part I: background and development. Natural Computing, 2007, 6(4): 467-484
[2]	Banks A, Vincent J, Anyakoha C. A review of particle swarm optimization Part II: hybridisation, combinatorial, multicriteria and constrained optimization, and indicative applications. Natural Computing, 2007, 7(1): 109-124
[3]	Shi Y H, Eberhart R C. A modified particle swarm optimizer. In: Proceedings of the 1998 IEEE International Conference on Evolutionary Computation. Anchorage, AK: IEEE, 1998. 69-73
[4]	Shi Y H, Eberhart R C. Paramter selection in particle swarm optimization. Evolutionary Programming VII. Berlin, Germany: Springer-Verlag, 1997. 591-600
[5]	Eberhart R C, Shi Y H. Particle swarm optimization: developments, applications and resources. In: Proceedings of IEEE International Conference on Evolutionary Computation. Seoul: IEEE, 2001, 1: 81-86
[6]	Shi Y S, Eberhart R C. Empirical study of particle swarm optimization. In: Proceedings of the IEEE International Congress on Evolutionary Computation. Washington DC: IEEE, 1999, 3: 591-600
[7]	Eberhart R C, Shi Y S. Tracking and optimizing dynamic systems with particle swarms. In: Proceedings of the IEEE Congress on Evolutionary Computation. Seoul, Korea: IEEE, 2001, 3: 94-97
[8]	Bin J, Lian Z G, Gu X S. A dynamic inertial weight particle swarm optimization algorithm. Chaos Solitons & Fractals, 2008, 37(3): 698-705
[9]	Ratnaweera A, Halgamuge S K, Watson H C. Self-organizing hierachical particle swarm optimizer with time-varying acceleration coefficients. IEEE Transactions on Evolutionary Computation, 2004, 8(3): 240-255
[10]	Zhan Z H, Zhang J, Li Y, Chung H S H. Adaptive particle swarm optimization. IEEE Transactions on Systems, Man, and Cybernetics-Part B, 2009, 39(6): 1362-1381
[11]	Kennedy J. The behavior of particles. In: Proceedings of the 7th International Conference on Evolutionary Programming. San Diego: IEEE, 1998. 579-589
[12]	van den Bergh F, Engelbrecht A P. A study of particle swarm optimization particle trajectories. Information Sciences, 2006, 176(8): 937-971
[13]	Clerc M. Stagnation Analysis in Particle Swarm Optimisation or What Happens When Nothing Happens, Technical Report CSM-460, Department of Computer Science, University of Essex, 2006
[14]	Chen J, Pan F, Cai T, Tu X Y. Stability analysis of particle swarm optimization without Lipschitz constraint. Journal of Control Theory and Applications, 2003, 1(1): 86-90
[15]	Trelea I C. The particle swarm optimization algorithm: convergence analysis and parameter selection. Information Processing Letters, 2003, 85(6): 317-325
[16]	Kadirkamanathan V, Selvarajah K, Fleming P J. Stability analysis of the particle dynamics in particle swarm optimizer. IEEE Transactions on Evolutionary Computation, 2006, 10(3): 245-255
[17]	Fernández-Martínez J L, García-Gonzalo E, Fernández-Alvarez J P. Theoretical analysis of particle swarm trajectories through a mechanical analogy. International Journal of Computational Intelligence Research, 2008, 4(2): 93-104
[18]	Clerc M, Kennedy J. The particle swarm: explosion, stability and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation, 2002, 6(1): 58-73
[19]	Jiang M, Luo Y P, Yang S Y. Stochastic convergence analysis and parameter selection of the standard particle swarm optimization algorithm. Information Processing Letters, 2007, 102(1): 8-16
[20]	Chen Y P, Jiang P. Analysis of particle interaction in particle swarm optimization. Theoretical Computer Science, 2010, 411(21): 2101-2115
[21]	Jin Xin-Lei, Ma Long-Hua, Wu Tie-Jun, Qian Ji-Xin. Convergence analysis of the particle swarm optimization based on stochastic processes. Acta Automatica Sinica, 2007, 33(12): 1263-1268 (in Chinese)
[22]	Su Shou-Bao, Cao Xi-Bin, Kong Min. Stability analysis of particle swarm optimization using swarm activity. Control Theory & Application, 2010, 27(10): 1411-1417 (in Chinese)
[23]	Zhou Xiao-Jun, Yang Chun-Hua, Gui Wei-Hua. Particle swarm optimization algorithm with variable random function. In: Proceedings of the 30th Chinese Control Conference. Yantai, China: IEEE, 2011. 5408-5412
[24]	Shi Y S, Eberhart R C. Population diversity of particle swarms. In: Proceedings of the IEEE Congress on Evolutionary Computation. Hong Kong, China: IEEE, 2008. 1063-1067
[25]	Andrews P S. An investigation into mutation operators for particle swarm optimization. In: Proceedings of the 2006 IEEE Congress on Evolutionary Computation. Vancouver, BC: IEEE, 2006. 1044-1051
[26]	Liang J J, Qin A K. Suganthan P N, Baskar S. Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE transaction on Evolutionary Computation, 2006, 10(3): 281-295
[27]	Deb K, Goyal M. A combined genetic adaptive search (GeneAS) for engineering design. Computer Science and Informatics, 1996, 26(4): 30-45
[28]	Yao X, Liu Y, Lin G M. Evolutionary programming made faster. IEEE Transactions on Evolutionary Computation, 1999, 3(2): 82-102
[29]	Suganthan P N, Hansen N, Liang J J, Deb K, Chen Y P, Auger A, Tiwari S. Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization. In: Proceedings of the IEEE Congress on Evolutionary Computation. Edinburgh, UK: IEEE, 2005. 1-50