切換式自我學習粒子群演算法

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：74

、訪客IP：3.137.41.153

姓名

曾柏憲(ZENG. BO SIAN) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

切換式自我學習粒子群演算法
(Switching Self-Learning Particle Swarm Optimization)

相關論文

★ 小型化 GSM/GPRS 行動通訊模組之研究	★ 語者辨識之研究
★ 應用投影法作受擾動奇異系統之強健性分析	★ 利用支撐向量機模型改善對立假設特徵函數之語者確認研究
★ 結合高斯混合超級向量與微分核函數之語者確認研究	★ 敏捷移動粒子群最佳化方法
★ 改良式粒子群方法之無失真影像預測編碼應用	★ 粒子群演算法應用於語者模型訓練與調適之研究
★ 粒子群演算法之語者確認系統	★ 改良式梅爾倒頻譜係數混合多種語音特徵之研究
★ 利用語者特定背景模型之語者確認系統	★ 智慧型遠端監控系統
★ 正向系統輸出回授之穩定度分析與控制器設計	★ 混合式區間搜索粒子群演算法
★ 基於深度神經網路的手勢辨識研究	★ 人體姿勢矯正項鍊配載影像辨識自動校準及手機接收警告系統

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

本篇論文中，我們提出一種改良式的粒子群演算法，名為切換式自我學習粒子群演算法(Switching Self-Learning Particle Swarm Optimization, SSLPSO)，利用切換式的方式，在不同時期運用不同的速度更新公式，使運算量降到最低，並加入了上升及下降函數，使其收斂速度能更快，前期粒子的多樣性足夠，後期粒子運用自我學習機制，學習群體中表現最佳者，讓解不落入區域最佳解，搜尋到全域最佳解。我們最後利用16個測試函數進行模擬，與其他已提出的19種改良式粒子群演算法做比較，實驗結果得知，本論文提出新的改良方法，能夠在大部分的測試函數中有較優越的表現。並且比較其中4種演算法利用MATLAB進行運算搜尋解的時間，分析可得我們論文不但具備「精確」，更擁有「快速」的優點，也就是省時。

摘要(英)

In this thesis, we propose a new particle swarm algorithm called Switching Self-Learning Particle Swarm Optimization (SSLPSO), which switches to different velocity updating formulas in different stages(periods), so the amount of calculation can be minimized. By adding "the rise and fall functions", the convergence rate can be faster. While the diversity of the particles are abundent at the beginning, the particles apply self-learning method at the later stage to learn from those who have the best performance, thus not falling into local optimum but reaching the global optimal. The 16 benchmark functions are used in the simulation of our proposed. The experimental results show that the proposed SSLPSO performs better on most of the functions, compared with other 19 improved particle swarm algorithms. We also analize 4 algorithms with MATLAB to see which can find the solution faster, and the result shows that the proposed SSLPSO is not only more accurate but also more efficient, which means more time can be saved.

關鍵字(中)

★ 粒子群演算法
★ 切換式
★ 非線性函數

關鍵字(英)

★ Particle Swarm Optimization
★ Switching
★ nonlinear

論文目次

摘要 I
Abstract V
目錄 VII
圖目錄 IX
表目錄 XII
第一章粒子群演算法 1
1.1研究動機 1
1.2論文架構 3
第二章緒論 4
2.1傳統粒子群演算法介紹 4
2.2粒子群演算法的基本公式 4
2.3慣性權重 5
第三章改良式自我學習粒子群演算法 10
3.1引言 10
3.2自我學習機制與原型 10
3.3演算法的切換機制 15
3.3.1高斯函數 15
3.3.2轉換函數fT 17
3.4切換式自我學習粒子群演算法 18
3.5上升及下降函數有無的比較 21
第四章實驗結果 35
4.1目標函數 35
4.2參數設定與測試方法 39
4.2.1測試函數在10維下的結果 40
4.2.2測試函數在30維下的結果 51
第五章演算法的時間比較 62
第六章總結與未來展望 67
6.1總結 67
6.2未來展望 67
參考文獻 68

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] 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.
[3] 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.
[4] 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.
[5] 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.
[6] 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.
[7] 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.
[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] 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
[10] Y. Shi and R. C. Eberhart, “Evolutionary Programming VII, Parameter Selection in Particle Swarm Optimization,” Springer Berlin Heidelberg, Vol. 1447, pp. 591–600, 1998.
[11] 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.
[12] 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.
[13] 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.
[14] Y. Shi and R. C. Eberhart, “Evolutionary Programming VII, Parameter Selection in Particle Swarm Optimization,” Springer Berlin Heidelberg, Vol. 1447, pp. 591–600, 1998.
[15] 吳讚展，「自調整非線性慣性權重粒子群演算法」，國立中央大學，碩士論文，民國101年。
[16] 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.
[17] I. C. Trelea, “The particle swarm optimization algorithm: convergence analysis and parameter selection,” Elsevier Science B.V., Vol. 85, pp. 317-325, 2003.
[18] W. H. Ip, D. Wang, and V. Cho, “Aircraft ground service scheduling problems and their genetic algorithm with hybrid assignment and sequence encoding scheme,” IEEE Systems Journal, Vol. 7, No. 4, 2013.
[19] 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.
[20] 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: Cybernetics, Vol. 26, No. 1, pp. 29, 41, 1996.
[21] .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.
[22] 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.
[23] 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.
[24] 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.
[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 adaptive time-varying Topology connectivity,” Applied Soft Computing , Vol. 24, pp. 623-642, 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] W. H. Lim, “Particle swarm optimization with increasing topology connectivity,” Engineering Applications of Article Intelligence, Vol. 27, pp. 80-102, 2014.
[30] 張伯墉，「適應性自我學習粒子群演算法」，桃園市：國立中央大學，碩士論文，民國105年。
[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] 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.
[33] P. N. Suganthan, N. Hansen, J. J. Liang and K. Deb, Y. -P. Chen, A. Auger & S. Tiwari, “Problem definitions and evaluation criteria for the CEC2005 special session on real-parameter optimization,” Technical report of Nanyang Technological University, 2005.
[34] M. Pant, T. Radha and V. P. Singh, “A New Particle Swarm Optimization with Quadratic Interpolation,” International Conference on Computational Intelligence and Multimedia Applications, pp. 55-60, 2007.
[35] 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.
[36] 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.
[37] Meysam Rahmati, Reza Effatnejad and Amin Safari, “Comprehensive Learning Particle Swarm Optimization (CLPSO) for Multi-objective Optimal Power Flow,” Indian Journal of Science and Technology, Vol 7(3), 262–270, March 2014
[38] 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.
[39] 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.
[40] 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。
[41] 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。
[42] 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.
[43] 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。
[44] 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。
[45] 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。
[46] Yuhui Shi and Russell Eberhart, “A Modified Particle Swarm Optimizer,” In Department of Electrical Engineering Indiana University Purdue University Indianapolis, pp. 69-73, 1998.
[47] YingMin Wang, Tao Cui ,FuJun Zhang, TianPu Dong, “Fault Diagnosis of Diesel Engine Lubrication System Based on PSO-SVM and Centroid Location Algorithm,” In 2016 International Conference on Control, Automation and Information Sciences (ICCAIS), pp. 221-226, 2016.
[48] Poonam Agarkar, Pratik Hajare, Narendra Bawane, “Optimization of Generalized Regression Neural Networks Using PSO and GA for Non-Performer Particles,” IEEE International Conference On Recent Trends In Electronics Information Communication Technology, pp. 103-107, 2016.
[49] Wei Gao and Zhirong Guo, “Research of Recurrent Wavelet Neural Network Speed Controller Based on Chaotic Series Adaptive PSO,” International Conference on Information Management, pp. 470-475, 2017.
[50] P. N. Sugant han, “Particle Swarm Optimiser with Neighbourhood Operator,” Department of Computer Science and Electrical Engineering University of Queensland St Lucia QLD 4072, Australia,” pp. 1958-1962, 1999.

指導教授

莊堯棠

審核日期

2017-7-26

推文