參考文獻 |
[1] M. Dorigo, V. Maniezzo, and A. Colorni, “Ant system: optimization by a colony of cooperating agents,” IEEE Transactions on Systems, Man, and Cybernetics-Part B, Vol. 26, No. 1, pp. 29-41, 1996.
[2] A. Colorni, M. Dorigo, and V. Maniezzo, “Distributed optimization by ant colonies,” Proceedings of ECAL′91-European Conference on Artificial Life, Paris, France, pp. 134-142, 1991.
[3] M. Dorigo, V. Maniezzo, and A. Colorni, “The ant system: an autocatalytic optimizing process,” Technical Report TR91-016, Dipartimento di Elettronica,Politecnico di Milano, Italy, 1991.
[4] J. Kennedy and R. Eberhart, “Particle swarm optimization,” Proceedings of the 4th IEEE International Conference on Neural Networks, Perth, WA, Australia, pp. 1942-1948, November/ December 1995.
[5] Y. Shi and R. C. Eberhart, “Evolutionary Programming VII, Parameter Selection in Particle Swarm Optimization,” Springer Berlin Heidelberg, Vol. 1447, pp. 591–600, 1998.
[6] 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.
[7] 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.
[8] 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.
[9] 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.
[10] J. Kennedy and W. M. Spears, “Matching algorithms to problems: An experimental test of the particle swarm and some genetic algorithms on the multimodal problem generator,” The 1998 IEEE International Conference on Evolutionary Computation Proceedings, pp. 78-83, 1988.
[11] 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.
[12] 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.
[13] V. Miranda and N. Fonseca, “EPSO-evolutionary particle swarm optimization, a new algorithm with applications in power systems,” Proceedings of IEEE Transmission Distribution Conference and Exhibition, Vol. 2, pp. 745-750, 6-10 October 2002.
[14] M. O’Neil and A. Brabazon, “Self-organizing swarm (SOSwarm): a particle swarm algorithm for unsupervised learning,” Proceedings of IEEE congress on Evolutionary Computation, Vancouver, BC, Canada, pp. 634-639, 16-21 July 2006.
[15] V. Miranda and N. Fonseca, “EPSO-best-of-two-worlds meta-heuristic applied to power system problems,” Proceedings of IEEE congress on Evolutionary Computation, Honolulu, HI, pp. 12-17, 12-17 May 2002.
[16] K. E. Parsopoulos and M. N. Vrahatis, “UPSO: a unified particle swarm scheme,” Proceedings of the International Conference of Computational Methods in Sciences and Engineering, Lecture Series on Computer and Computational Sciences, Vol. 1, pp. 868-873, 2004.
[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] 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.
[19] J. Kennedy, R. Eberhart, and Y. Shi, Swarm Intelligence, California: Morgan Kauffman, 2001.
[20] V. Miranda, “Evolutionary algorithm with particle swarm movements,” Proceedings of the 13th International Conference on Intelligent Systems Application to Power Systems, Arlington, VA , pp. 6-21, 6-10 November 2005.
[21] C. Liu and C. Ouyang, “An adaptive fuzzy weight PSO algorithm,” 2010 Fourth International Conference on Genetic and Evolutionary Computing, pp. 8, 10, 13-15, 2010.
[22] 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.
[23] W. H. Lim, “Particle swarm optimization with adaptive time-varying Topology connectivity,” Applied Soft Computing , Vol. 24, pp. 623-642, 2014.
[24] K. E. Parsopoulos and M.N. Vrahatis, “On the computation of all global minimizers through particle swarm optimization,” IEEE Transactions on Evolutionary Computation, Vol. 8, No. 3, pp. 211-224, 2004.
[25] Lin Chuan, Feng Quanyuan. “The Standard Particle Swarm Optimization Algorithm Convergence Analysis and Parameter Selection” School of Information Science and Technology, Southwest Jiaotong University, Chengdu,610031, China, Vol. 2007
[26] I. C. Trelea, “The particle swarm optimization algorithm: convergence analysis and parameter selection,” Elsevier Science B.V., Vol. 85, pp. 317-325, 2003.
[27] 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.
[28] Y.-T. Juang, S.-L. Tung, and H.-C. Chiu, “Adaptive fuzzy particle swarm optimization for global optimization of multimodal functions,” Information Sciences, in press, doi: 10.1016/j.ins.2010.11.025.
[29] S. T. Hsieh, T. Y. Sun, C. C. Liu, and S. J. Tsai, “Efficient population utilization strategy for particle swarm optimizer,” IEEE Transactions on Systems, Man, and Cybernetics, Part B, Vol. 39, No. 2, pp. 444-456, 2009.
[30] X. Yao, Y. Liu, and G. Lin, “Evolutionary programming made faster,” IEEE Transactions on Evolutionary Computation, Vol. 3, No. 2, pp. 82-102, 1999.
[31] R. Mendes, J. Kennedy, and J. Neves, “The fully informed particle swarm: simpler, maybe better,” IEEE Transactions on Evolutionary Computation, Vol. 8, No. 3, pp. 204-210, 2004.
[32] 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.
[33] 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.
[34] 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 CEC 2005 special session on real-parameter optimization,” Technical report, Nanyang Technological University, Singapore, 2005.
[35] R. Salomon, “Reevaluating genetic algorithm performance under coordinate rotation of benchmark functions,” BioSystems, Vol. 39, No. 3, pp. 263-278, 1996.
[36] Y. Shi and R. Eberhart, “A modified particle swarm optimizer,” Proceedings of IEEE congress on Evolutionary Computation, Anchorage, AK , USA, pp. 69–73, 4-9 May 1998.
[37] P. J. Angeline, “Using selection to improve particle swarm optimization,” Proceedings of IEEE congress on Evolutionary Computation, Anchorage, AK , USA, pp. 84-89, 4-9 May 1998.
[38] N. Iwasaki, K. Yasuda, and G. Ueno, “Dynamic parameter tuning of particle swarm optimization,” IEEJ Transactions on Electrical and Electronic Engineering, Vol. 1, No. 4, pp. 353-363, 2006.
[39] J. J. Liang, P. N. Suganthan, and K. Deb, “Novel composition test functions for numerical global optimization,” Proceedings of IEEE on Swarm Intelligence Symposium, pp. 68-75, 8-10 June 2005.
[40] 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.
[41] M. Pant, T. Radha, and V. P. Singh, “A new particle swarm optimization with quadratic interpolation,” Proceedings of IEEE International Conference on Computational Intelligence and Multimedia Applications, Sivakasi, Tamil Nadu, pp. 55-60, 13-15 December 2007.
[42] M. A. Montes de Oca, J. Pena, T. Stutzle, C. Pinciroli, and M. Dorigo, “Heterogeneous particle swarm optimizers,” Proceedings of IEEE congress on Evolutionary Computation, Trondheim , pp. 698-705, 18-21 May 2009.
[43] J. J. Liang and P. N. Suganthan, “Dynamic multi-swarm particle swarm optimizer,” Proceedings of IEEE on Swarm Intelligence Symposium, pp.124-129, 8-10 June 2005.
[44] W. Du and B. Li, “Multi-strategy ensemble particle swarm optimization for dynamic optimization,” Information Sciences, Vol. 178, No. 15, pp. 3096-3109, 2008.
[45] J. J. Liang, A. K. Qin, P. N. Suganthan, and S. Baskar, “Comprehensive learning particle swarm optimizer for global optimization of multimodal functions,” IEEE Transactions on Evolutionary Computation, Vol. 10, No. 3, pp. 281-296, 2006.
[46] Y. Wang and Y. Yang, “Particle swarm optimization with preference order ranking for multi-objective optimization,” Information Sciences, Vol. 179, No. 12, pp. 1944-1959, 2009.
[47] D. Hrovat, “Survey of advanced suspension developments and related optimal control applications,” Automatica, Vol. 33, No. 10, pp. 1781-1817, 1997.
[48] C. O. Ourique, E. C. Biscaia, and J. C. Pinto, “The use of particle swarm optimization for dynamic analysis in chemical processes,” Computers & Chemical Engineering, Vol. 26, No. 12, pp. 1783-1793, 2002.
[49] F. Heppner and U. Grenander, “A stochastic nonlinear model for coordinated bird flocks”, The Ubiquity of Chaos, AAAS Publications , pp. 233-238, 1990.
[50] W. Gao and Z. Guo, “Research of Recurrent Wavelet Neural Network Speed Controller Based on Chaotic Series Adaptive PSO,” International Conference on Information Management, pp. 470-475, 2017.
|