參考文獻 |
[1] J. Kennedy and R. C. Eberhart, “Particle Swarm Optimization,” In Proceedings of IEEE International Conference on Neural Networks,” Vol. IV, pp. 1942−1948, 1995.
[2] W. D. Chang and S. P. Shih, “PID controller design nonlinear systems using an improved particle swarm optimization approach,” Communication Nonlinear Science and Numerical Simulation, Vol. 15, pp. 3632-3639, 2010.
[3] R. A. Krohling and L. S. Coelho, “Coevolutionary Particle Swarm Optimization Using Gaussian Distribution for Solving Constrained Optimization Problem,” IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics , Vol. 36, No. 6, pp. 1407-1416, 2006.
[4] G. Zeng and Y. Jiang, “A Modified PSO Algorithm with Line Search,” In Proceedings of 2010 International Conference on Computational Intelligence and Software Engineering, pp. 1-4, 2010.
[5] H. Babaee and A. Khosravi, “An Improve PSO Based Hybrid Algorithms,” In Proceedings of 2011 International Conference on Management and Service Science, pp. 1-5, 2011.
[6] S. Y. Ho, H. S. Lin, W. H. Liauh, and S. J. Ho, “OPSO: Orthogonal particle swarm optimization and its application to task assignment problems,” IEEE Transactions on Man and Cybernetics, Part A: Systems and Humans, Vol. 38, No. 2, pp. 288-298, 2008.
[7] Y. Shi and R. C. Eberhart, “Evolutionary Programming VII, Parameter Selection in Particle Swarm Optimization,” Springer Berlin Heidelberg, Vol. 1447, pp. 591–600, 1998.
[8] M. Clerc, “The Swarm and the Queen: Towards a Deterministic and Adaptive Particle Swarm Optimization,” In Proceedings of the Congress on Evolutionary Computation, Vol. 3, pp. 1951−1957, 1999.
[9] M. Clerc and J. Kennedy, “The Particle Swarm—Explosion, Stability, and Convergence in a Multidimensional Complex Space,” IEEE Transactions on Evolutionary Computation, Vol. 6, No. 1, pp. 58-73, 2002.
[10] A. Ratnaweera, S. K. Halgamuge, and H. C. Watson, “Self-Organizing Hierarchical Particle Swarm Optimizer with Time-Varying Acceleration Coefficients,” IEEE Transactions On Evolutionary Computation, pp. 240-255, 2004.
[11] N. M. Kwok, D. K. Liu, K. C. Tan, and Q. P. Ha, “An Empirical Study on the Settings of Control Coefficients in Particle Swarm Optimization,” In Proceedings of IEEE Congress on Evolutionary Computation, pp. 823-830, 2006.
[12] J. Wei, L. Guangbin and L. Dong, “Elite Particle Swarm Optimization with Mutation,” In Proceedings of 2008 Asia Simulation Conference-7th International Conference on System Simulation and Scientific Computing, pp. 800-803, 2008.
[13] Y. -T Juang, S. -L. Tung, and H. -C. Chiu, “Adaptive fuzzy particle swarm optimization for global optimization of multimodal functions,” International Journal of Information Sciences, Vol. 181, pp. 4539-4549, 2011.
[14] M. R. Tanweer, S. Suresh, and N. Sundararajan, “Self regulating particle swarm optimization algorithm,” International Journal of Information Sciences, pp. 182-202, 2015.
[15] Y. Shi and R. C. Eberhart, “Particle Swarm Optimization:Development, Applications and Resource,” In Proceedings of the 2001 Congress on Evolutionary Computation, Vol. 1, pp. 81-86, 2001.
[16] 吳讚展,「自調整非線性慣性權重粒子群演算法」,桃園市:國立中央大學,碩士論文,民國101年。
[17] Y. Shi and R. C. Eberhart, “Empirical Study of Particle Swarm Optimization,” In Proceedings of the 1999 Congress on Evolutionary Computation, Vol. 3, pp. 1945-1950, 1999.
[18] I. C. Trelea, “The particle swarm optimization algorithm: convergence analysis and parameter selection,” Elsevier Science B.V., Vol. 85, pp. 317-325, 2003.
[19] J. Kennedy, R. C. Eberhart, and Y. Shi, “Swarm intelligence,” Morgan Kaufmann Publishers, San Francisco, 2001.
[20] T. -H Kim, I. Maruta, and T. Sugie, “Robust PID controller tuning based on the constrained particles swarm optimization,” Automatica, Vol. 44, no. 4, pp.1104-1110, 2008.
[21] A. W. Mohemmed, Z. Mengjie, and N. C. Sahoo, “A new particle swarm optimization based algorithm for solving short-paths tree problems,” In Proceedings of IEEE Congress on Evolutionary Computation, pp. 3221-3225, 2007.
[22] J. P. Papa, L. M. G. Fonseca, and L. A. S. de Carvalho, “Projections onto convex sets through particle swarm optimization and its application for remote sensing image restoration,” Pattern Recognition Letters. Vol. 31, pp. 1876-1886, 2010.
[23] 米勒(Peter Miller),林俊宏譯,《群的智慧:向螞蟻、蜜蜂、飛鳥學習組織運作技巧》(The smart swarm: how understanding flocks, schools and colonies can make us better at communicating, decision making, and getting things done),臺北市:天下遠見,2010。
[24] W. H. Lim, “Particle swarm optimization with adaptive time-varying Topology connectivity,” Applied Soft Computing , Vol. 24, pp. 623-642, 2014.
[25] J. Kennedy and R. Eberhart, “The particle swarm optimization: Social adaptation of knowledge,” In Proceedings of the International conference on Evolutionary Computation, pp. 303-308, 1997.
[26] W. H. Lim, “Particle swarm optimization with increasing topology connectivity,” Engineering Applications of Article Intelligence, Vol. 27, pp. 80-102, 2014.
[27] D. Chen, F. Zou, Z. Li, J. Wang, and S. Li, “An improved teaching-learning-based optimization algorithm for solving global optimization problem,” International Journal of Information Sciences, Vol. 297, pp. 179-190, 2015.
[28] R. Mendes, J. Kennedy, and J. Neves, “The fully informed particle swarm:simpler, maybe better,” IEEE Transactions on Evolutionary Computation, Vol. 8, pp. 204-210, 2004.
[29] P. N. Suganthan, N. Hansen, J. J. Liang, K. Deb, Y. -P. Chen, A. Auger, and S. Tiwari, “Problem definitions and evaluation criteria for the CEC2005 special session on real-parameter optimization,” Technical report of Nanyang Technological University, 2005.
[30] N. Iwasaki, K. Yasuda, and G. Ueno, “Dynamic parameter tuning of particle swarm optimization,” IEEE Transactions on Electrical and Electronic Engineering, pp. 353-363, 2006.
[31] M. A. Montes de Oca, J. Pena, T. Stutzle, C. Pinciroli, and M. Dorigo, “Heterogeneous particle swarm optimizers,” In Proceedings of IEEE Congress on Evolutionary Computation, pp. 698–705, 2009.
[32] M. Pant, T. Radha, and V. P. Singh, “A New Particle Swarm Optimization with Quadratic Interpolation,” In Proceedings of International Conference on Computational Intelligence and Multimedia Applications, pp. 55-60, 2007.
[33] K. E. Parsopoulos and M. N. Vrahatis, “UPSO: a unified particle swarm optimization scheme,” In Lecture series on Computer and Computational Sciences, Vol. 1, pp. 868-873, 2004.
[34] J. J. Liang, and P. N. Suganthan, “Dynamic multi-swarm particle swarm optimizer,” In Proceedings of IEEE on Swarm Intelligence Symposium, pp. 124-129, 2005
[35] 陳珈妤,「快速平衡粒子群最佳化方法」,桃園市:國立中央大學,碩士論文,民國100年。
[36] 蔡憲文,「以時變學習因子策略改良粒子群演算法」,桃園市:國立中央大學,碩士論文,民國99年。
[37] A. Chatterjee and P. Siarry, “Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization,” Computers and Operations Research, Vol. 33, No. 3, pp. 859-871, 2004.
[38] 李憲昌,「維度經驗重心分享粒子群演算法」,桃園市:國立中央大學,碩士論文,民國102年。
[39] 顏淯翔,「改良式粒子群方法之影像追蹤系統應用」,桃園市:國立中央大學,碩士論文,民國103年。
[40] 王鈺潔,「自適應解分享粒子群演算法及其在螺旋電感最佳化設計之應用」,桃園市:國立中央大學,碩士論文,民國104年。
[41] M. R. Anderberg, “Cluster Analysis far Application,” Academic Press, New York, 1973.
[42] J. Han and M. Kamber, “Data Mining: Concepts and Techniques,” Morgan Kaufmann, 2000.
[43] K. Cios, W. Pedrycs, and R. Swiniarski, “Data Mining – Methods for Knowledge Discovery,” Kluwer Academic Publishers, 1998.
[44] M. Omran, A. Salman, and A. P. Engelbrecht, “Image Classification using Particle Swarm Optimization,” In Proceedings of the Conference on Simulated Evolution and Learning, pp. 370-374, 2002.
[45] J. B. MacQueen, “Some methods for classification and analysis of multivariate observations,” In: Proceedings of the Fifth Berkeley Symp. Math. Stat. Prob., pp. 281-297, 1967.
[46] A. K. Jain, M. N. Murty, and P. J. Flynn, “Data clustering: a review,” ACM Computing Surveys, pp. 264-323, 1999.
[47] X. Cui and T. E. Potok, “Document Clustering Analysis Based on Hybrid PSO+K-means Algorithm,” Journal of Computer Sciences (Special Issue), pp. 27-33, 2005.
[48] 楊正宏、蕭智仁和莊麗月,K-means結合混沌PSO應用於資料分群問題,台北市:ICIM2009 第二十屆國際資訊管理學術研討會,pp. 218-227,2009。
[49] D. W. van der Merwe, and A. P. Engelhrecht, “Data Clustering using Particle Swarm Optimization,” In Proceedings of the IEEE Congress on Evolutionary Computation, pp. 215-220, 2003.
[50] A. Abraham, S. Das, and S. Roy, “Swarm Intelligence Algorithms for Data Clustering,” In Soft Computing for Knowledge Discovery and Data Mining, pp. 279-313, 2008. |