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標題: 粒子群演算法之改善及探討
Exploration of improvement of particle swarm optimization
作者: 紀梓民
Ji, Zih-Ming
關鍵字: optimization;最佳化設計;partcle swarm optimization;粒子群演算法
出版社: 機械工程學系所
引用: [1] J. Kennedy and R. C. Eberhart, “Particle swarm optimization,” Proc. IEEE Int. Conf. Neural Networks, pp. 1942-1948, 1995. [2] M. M. Millonas,“Swarm,phasetransitions,and collective intelligence,” C. G .Langton, Ed., Artificial Life III. Addison Wesley, Reading, MA. 1994. [3] V. Gerhard and S. S. Jaroslaw, “Particle swarm optimization,” Source: Collection of Technical Papers-AIAA/ASME/ASCE/AHS/ASC Structures ,Structural Dynamics and Materials Conference, Vol. 1, pp.282-290, 2002. [4] R. C. Eberhart and J. Kennedy, “A new optimizer using particle swarm theory,” Proceedings of the Sixth International Symposium on Micromachine and Human Science, Nagoya, Japan, pp. 39-43, 1995. [5] R. C. Eberhart and Y. Shi, “Particle swarm optimization: development, applications and resources,” Evolutionary Computation, 2001. Proceedings of the 2001 Conqress on Vol. 1, pp. 81-86, 2001. [6] R. C. Eberhart and Y. Shi, “A modified particle swarm optimizer,” Evolutionary Computation Proceedings, 1998. IEEE World Conqress on Computational Itelligence. The 1998 IEEE International Conference on, pp. 69-73, May, 4-9, 1998. [7] L. Wang, Q. Kang, H. Xiao, and Q. Wu, “A Modified adaptive particle swarm optimization algorithm,”Proceedings of the IEEE International Conference on Industrial Technology, vol. 2005, 2005 IEEE International Conference on industrial Technology, ICIT 2005, pp. 209-214, 2005. [8] F. van den Bergh and A. P. Engelbrecht, “A study of particle swarm optimization particle trajectories,” Information Sciences, Vol. 176, No. 8, pp. 937-971, April, 22, 2006. [9] P. C. Fourie and A. A. Groenwold, “The particle swarm optimization algorithm in size and shape optimization,” Source: Structural and Multidisciplinary Optimization, Vol. 23, No. 4, pp. 259-267, May, 2002. [10] Z. Qingfu, J. Sun, E. Tsang and J. Ford, “Hybrid estimation of distribution algorithm for global optimization,” Source: Engineering Computations (Swansea, Wales), Vol. 21, No. 1, pp. 91-107, 2004. [11] C. A. C. Coello, G. T. Pulido and M. S. Lechuga, “Handling multiple objectives with particle swarm optimization,” Source: IEEE Transactions on Evolutionary Computation, Vol. 8, No. 3, pp. 256-279, June, 2004 [12] S. L. Ho, S. Yang, G. Ni and H. C. Wong, “A particle swarm optimization method with enhanced global search ability for design optimizations of electromagnetic devices,” Source: IEEE Transactions on Magnetics, Vol. 42, No. 4, pp. 1107-1110, April, 2006. [13] J. J. Liang, A. K. Qin, P. N. Suganthan and S. Baskar, “Comprehensive learning particle swarm optimizer for global optimization of multimodal functions,” Source: IEEE Transactions on Evolutionary Computation, Vol. 10, No. 3, pp. 281-295, June, 2006. [14] A. Stacey, M. Jancic, I. Grundy; “Particle swarm optimization with mutation,” Evolutionary Computation, 2003. CEC ''03. The 2003 Congress on Vol. 2, pp. 1425 – 1430, December, 8-12, 2003. [15] H. J. Meng, Z. Peng, R. Y. Wu, X. J. Hao and Z. Xie, “A hybrid particle swarm algorithm with embedded chaotic search,” Source: 2004 IEEE Conference on Cybernetics and Intelligent Systems, 2004 IEEE Conference on Cybernetics and Intelligent Systems, pp. 367-371, 2004. [16] 陳信昌, “可處理離散和混合變數之演化策略法,” 國立中興大學機械工程研究所碩士論文, 附錄A, 民國95年1月 [17] J. J. Liang, A. K. Qin, P. N. Suganthan and S. Baskar, “Evaluation of comprehensive learning particle swarm optimizer,” Springer’s Lecture Notes in Computer Science, ICONIP’04, Vol. 3316, pp. 230-235, 2004. [18] K. T. Fang, Y. Wang and P. M. Bentler, “Some applications of number-theoretic methods in statistics,” Statistical Science, Vol. 9, No. 3, pp. 416-428, August, 1994.


The particle swarm optimization (PSO) method has good performance and is easy to be programmed. Since it uses multiple particle to search the optimum solution, it has the better chance to find the global solution. Althogh it has those advantages mentioned, it consumes a lot of computation time to compute the fitnesses of particles and some parameters in PSOmay affect the solution significantly. According to this understanding, this thesis tries to modify PSO algorithm in order to improve its quality of solutions. The main approches include: using uniform design to ensure the uniform distribution of initial particles in the design space; adding mutation operation to increase the diversity of particles; decreasing the maximum velocity limitation and the velocity inertia automatically to balance the local and the global search efforts; developing a new approach to treat the design variables exceeding the bounds; using extensive local searches to escape local minimum. The overall effect of these approaches can yield better results for most test problems.

For original PSO, itcan only find a solution in a single run. For multi-modal problems, many runs are needed find different solutions. To overcome this drawback, this thesis also developes a method incorporated with original PSO to find many solutions in a single run for muti-modal problems.
其他識別: U0005-3101200813400500
Appears in Collections:機械工程學系所

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