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一种改进二进制编码量子行为粒子群优化聚类算法

, PP. 1463-1468

Keywords: 量子行为粒子群优化,二进制编码,完全学习策略,聚类

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Abstract:

为了改善二进制量子行为粒子群优化(BQPSO)算法的收敛性能,提出了一种基于完全学习策略的改进BQPSO优化(CLBQPSO)算法,并由此设计了一种新的数据聚类方法.该算法在4个测试数据集上与其他一些聚类算法进行了聚类实验比较,实验结果表明,基于CLBQPSO的聚类算法不仅收敛速度快,而且有较好的全局收敛性,收敛精度优于其他聚类算法,聚类效果更好.

References

[1]  Taher N, Babak A. An efficient hybrid approach based on PSO, ACO and k-means for cluster analysis [J]. Applied Soft Computing 10, 2010, 183-197. [2] 李峻金,向阳,芦英明,吴朔桐.粒子群聚类算法综述[J].计算机应用研究,2009,12,4423-4427. Li J J, Xiang Y, Lu Y M, Wu S T. Survey of particle swarm clustering algorithms [J]. Application Research of Computers, 2009, 12, 4423-4427. [3] Sun J, Feng B, Xu W B. Particle Swarm Optimization with Particles Having Quantum Behavior [C]. Proc. 2004 Congress on Evolutionary Computation, Piscataway, NJ, 2004, 325-331. [4] Lu Y, Lu S, Fotouhi F, Deng Y, Brown S. Incremental genetic K-means algorithm and its application in gene expression data analysis [J]. BMC Bioinformatics, 2004, 5, 172-181. [5] 周頔,孙俊,须文波.基于二进制具有量子行为的粒子群算法的多边形近似[J].计算机应用,2007,08,2030-2032. Zhou D, Sun J, Xu W B. Polygonal approximation of curves using binary quantum-behaved particle swarm optimization [J]. Journal of Computer Applications, 2007, 08, 2030-2032. [6] 奚茂龙,孙俊,耿汝年,须文波.基于二进制编码QPSO算法的移动机器人路径规划[J].系统仿真学报,2009,17,5516-5523. Xi M L, Sun J, Geng R N, Xu W B. Path Planning for Mobile Robot Based on Binary Quantum-behaved Particle Swarm Optimization [J]. Journal of System Simulation, 2009, 17, 5516-5523. [7] Kennedy J, Eberhart R. Particle Swarm Optimization [C]. Proceedings of IEEE International Conference on Neural Networks, Perth Australia 1995, USA: IEEE, 1995, 1942-1948. [8] Fang W, Sun J, Xu W B. Design IIR digital filters using quantum-behaved particle swarm optimization [C]. Int Conf on Natural Computation. Zurich: Springer-Verlag, 2006:637-640. [9] Sun J, Liu J, Xu W B. Using Quantum-behaved particle swarm optimization algorithm to solve non-linear programming problems [J]. Int J of computer Mathematics, 2007, 84(2), 261-272. [10] Chen W, Sun J, Ding Y R. Clustering of gene expression data with quantum-behaved particle swarm optimization [C]. IEA/AIE, Zurich: Springer-Verlag, 2008, 388-396. [11] Zhao J, Sun J, Chen W, Xu W B. Tracking Extrema in Dynamic Environments with Quantum-behaved Particle Swarm Optimization [C]. Proceedings of the 2009 WRI Global Congress on Intelligent Systems, 2009, v 2, 103-108. [12] 奚茂龙,孙俊,吴勇.一种二进制编码的量子粒子群优化算法[J].控制与决策,2010,01,99-104. Xi M L, Sun J, Wu Y. Quantum-behaved particle swarm optimization with binary encoding [J]. Control and Decision, 2010, 01, 99-104. [13] Ling J J, Qin A K. Comprehensive Learning Particle Swarm Optimizer for Global Optimization of Multimodal Functions [J]. IEEE Transactions on Evolutionary Computation, 2006, v 10, No. 3, 281-295. [14] Blake C, Keough E, Merz C J. UCI Repository of Machine Learning Database, 1998. [Online]. Available: http://archive.ics.uci.edu/ml/. [15] Bandyopadhyay S, Mukhopadhyay A, Maulik U. An improved algorithm for clustering gene expression data[J]. Bioinformatics, 2007, 23, 2859-2865.

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