[1] KENNEDY J, EBERHART R. Particle swarm optimization[C]//Proceedings of the 1995 IEEE International Conference on Neural Networks. Piscataway: IEEE, 1995,4:1942-1948. [2] LIANG 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 Computations, 2006,10(3):281-295. [3] HU W, LI Z. A simpler and more effective particle swarm optimization algorithm[J]. Journal of Software, 2007,18(4):861-868.(胡旺,李志蜀.一种更简化而高效的粒子群优化算法[J].软件学报,2007,18(4):861-868.) [4] WANG Y, LI J. Centroid particle swarm optimization algorithm[J]. Computer Engineering and Applications, 2011,47(3):34-37.(汪永生,李均利. 质心粒子群优化算法[J].计算机工程与应用,2011,47(3):34-37.) [5] SHI Y, EBERHART R C. Empirical study of particle swarm optimization[C]//Proceedings of the Congress on Evolutionary Computation. Piscataway: IEEE, 1999,3:1945-1950. [6] CLERC M, KENNEDY J. The particle swarm explosion, stability, and convergence in a multidimensional complex space[J]. IEEE Transactions on Evolutionary Computation, 2002,6(1):58-73. [7] EBERHART R C, SHI Y. Comparing inertia weights and constriction factors in particle swarm optimization[C]//Proceedings of the 2000 Congress on Evolutionary Computation. Piscataway: IEEE, 2000:84-88. [8] CHEN G, HUANG X, JIA J, et al. Natural exponential inertia weight strategy in particle swarm optimization[C]//Proceedings of the 6th World Congress on Control and Automation. Piscataway: IEEE, 2006,1:3672-3675. [9] MALIK R, RAHMAN T, HASHIM S, et al. New particle swarm optimizer with sigmoid increasing inertia weight[J]. International Journal of Computer Science and Security, 2007,1(2):35-44. [10] WU J, LIU W, ZHAO W, et al. Exponential type adaptive inertia weighted particle swarm optimization algorithm[C]//Proceedings of the 2nd International Conference on Genetic and Evolutionary Computing. Washington, DC: IEEE Computer Society, 2008:79-82. [11] ALATAS B, AKIN E, BEDRI OZER A. Chaos embedded particle swarm optimization algorithms[J]. Chaos, Solitons and Fractals, 2009,40(4):1715-1734. [12] PANT M, RADHA T, SINGH V P. Particle swarm optimization using Gaussian inertia weight[C]//Proceedings of the 2007 International Conference on Computational Intelligence and Multimedia Applications. Washington, DC: IEEE Computer Society, 2007,1:97-102. [13] CHATTERJEE A, SIARRY P. Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization[J]. Computer and Operations Research, 2006,33:859-871. [14] BANSAL J C, SINGH P K, SARASWAT M, et al. Inertia weight strategies in particle swarm optimization[C]//Proceedings of the 3rd World Congress on Nature and Biologically Inspired Computing. Piscataway: IEEE, 2011:640-647. [15] TING T, SHI Y, CHENG S, et al. Exponential inertia weight for particle swarm optimization[C]//Proceedings of the 3rd International Conference on Advances in Swarm Intelligence. Berlin: Springer-Verlag, 2012,1:83-90. [16] LU Z, HOU Z. Particle swarm optimization with adaptive mutation[J]. Acta Electronica Sinica, 2004,32(3):416-420.(吕振肃,侯志荣.自适应变异的粒子群优化算法[J].电子学报,2004,32(3):416-420.) [17] RIGET J, VESTERSTROEM J S. A diversity-guided particle swarm optimizer-the ARPSO[EB/OL].[2014-06-11]. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.19.2929&rep=rep1&type=pdf. [18] NIKNAM T, AMIRI B. An efficient hybrid approach based on PSO, ACO and k-means for cluster analysis[J]. Applied Soft Computing, 2010,10(1):183-197. [19] DERRAC J, GARICIA S, MOLINA D, et al. A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms[J]. Swarm and Evolutionary Computation, 2011,1(1):3-18. [20] HATAMLOU A. Black hole: a new heuristic optimization approach for data clustering[J]. Information Sciences, 2013,222:175-184. |