快速平衡粒子群最佳化方法

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：42

、訪客IP：3.142.156.97

姓名

陳珈妤(Chia-Yu Chen) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

快速平衡粒子群最佳化方法
(Swiftly balanced 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)

快速平衡粒子群最佳化方法(Swiftly balanced particle swarm optimization, SBSPO)是一種改良的粒子最佳化方法(Particle swarm optimization, PSO)，利用改變加速係數來平衡個體經驗及群體經驗，改善粒子最佳化方法易落入區域最佳解的缺點。利用粒子群收斂狀況決定加速係數大小，加速係數大小被設定為三段線性直線，一旦得知粒子群收斂狀況，則可求得一組適合的加速係數。因為能利用粒子群收斂狀況快速求得一組加速係數大小，又因這組加速係數能平衡個體經驗與群體經驗，因此名為快速平衡粒子群最佳化方法。本文也將二次內插演算法(Quadratic interpolation)與快速平衡粒子群最佳化方法(SBPSO)做結合，名為SBPSO-QI。另外提出考慮兩個群體最佳解來改良粒子最佳化方法，讓PSO在處理複雜問題時，能跳出區域最佳解，求得全域最佳解，並將此方法與SBPSO做結合，名為SBPSO-2G。並將提出的SBPSO、SBPSO-QI與SBPSO-2G與8種不同的粒子最佳化方法做比較。經模擬結果顯示，提出的方法對於多數的測詴函數均有較優越的表現。本文所提出的快速平衡粒子群最佳化方法保有粒子最佳化方法容易實現的特性，同時改良粒子最佳化方法易落入區域最佳解的缺點。

摘要(英)

Swiftly balanced particle swarm optimization (SBPSO) is a new variant of particle swarm optimization which can quickly balanced the personal and social experience. A new strategy of the acceleration coefficients makes SBPSO more effective, because the swarm can efficiently adjust the velocity by changing the acceleration coefficients. The acceleration coefficients of SBPSO are obtained by three segment line dependent on the swarm convergence. The advantage is that SBPSO become more accurate and also easy to implement. The acceleration coefficients of SBPSO can be applied to many variants of PSO. In this paper, incorporating the acceleration coefficients of SBPSO and The quadratic interpolation PSO, named SBPSO-QI. In the result section, compared the proposed SBPSO and SBPSO-QI with standard PSO (SPSO), quadratic interpolation PSO (QIPSO), unified PSO (UPSO), fully informed particle swarm (FIPS), dynamic multi-swarm PSO (DMSPSO), adaptive fuzzy PSO (AFPSO), and PSO with time-varying acceleration coefficients (PSO-TVAC) across sixteen benchmark functions.

關鍵字(中)

★ 粒子群最佳化方法

關鍵字(英)

★ particle swarm optimization

論文目次

中文摘要 ........................................................................................................................... i
英文摘要 ........................................................................................................................... ii
目錄 ................................................................................................................................. iii
圖目錄 ............................................................................................................................... v
表目錄 ............................................................................................................................. vii
一. 動機與架構 ................................................................................................................. 1
1-1 研究動機 ................................................................................................................ 1
1-2 本文架構 ................................................................................................................ 5
二. 簡介計算型智慧 ......................................................................................................... 6
2-1 計算型智慧 ............................................................................................................ 6
2-2 粒子最佳化方法 .................................................................................................... 7
2-3 基因演算法 .......................................................................................................... 11
三. 快速平衡粒子群最佳化方法 ................................................................................... 15
3-1快速平衡粒子群最佳化方法 ............................................................................... 15
3-1-1 訓練加速係數 ............................................................................................. 16
3-1-2 訓練結果 ..................................................................................................... 21
3-1-3快速平衡粒子群最佳化方法流程 .............................................................. 23
3-2快速平衡粒子群最佳化方法參數討論 ............................................................... 23
3-3快速平衡粒子群最佳化方法變形 ....................................................................... 26
3-3-1 SBPSO-QI .................................................................................................... 26
3-3-2 SBPSO-2G ................................................................................................... 28
四. 測詴模擬結果 ........................................................................................................... 32
4-1 測詴函數 .............................................................................................................. 32
4-2 10維測詴結果 ...................................................................................................... 36
4-3 30維測詴結果 ...................................................................................................... 43
iv
五. 快速平衡粒子群最佳化方法應用於資料分群 .......................................................
5-1 K-means ................................................................................................................ 50
5-2 利用粒子群最佳化方法做資料分群 .................................................................. 51
5-3 利用快速平衡粒子群最佳化方法與其變形做資料分群 .................................. 52
5-4 實際模擬結果 ...................................................................................................... 53
六. 總結與未來發展 ....................................................................................................... 57
6-1 總結 ...................................................................................................................... 57
6-2 未來發展 .............................................................................................................. 58
參考文獻 ......................................................................................................................... 59

參考文獻

﹝1﹞J. Kennedy & R. C. Eberhart, ―Particle swarm optimization,‖ In Proceedings of IEEE Conference on Neural Networks, pp. 1942-1948, 1995.
﹝2﹞Y. Shi & R. C. Eberhart, ―A modified particle swarm optimizer,‖ In Proceedings of IEEE international conference on evolutionary computation, pp. 69-73, 1998.
﹝3﹞邱鴻志，「粒子群演算法之調適性模糊邏輯控制」，國立中央大學，碩士論文，民國99年。
﹝4﹞A. Ratnaweera, S. K. Halgamuge & H. C. Watson, ―Self-Organizing Hierarchical Particle Swarm Optimizer With Time-Varying Acceleration Coefficients,‖ IEEE Transactions On Evolutionary Computation, pp. 240 - 255, 2004.
﹝5﹞Y. -T Juang, S. -L. Tung & H. -C. Chiu, ―Adaptive fuzzy particle swarm optimization for global optimization of multimodal functions,‖ International Journal of Information Sciences, 2010.
﹝6﹞H. Zhu, Y. Wang, K. Wang, Y. Chen, ―Particle Swarm Optimization (PSO) for the constrained portfolio optimization problem,‖ Expert Systems with Applications, pp. 10161-10169 , 2011.
﹝7﹞M. Pant, T. Radha & V.P.Singh, ―A new particle swarm optimization with quadratic interpolation,‖ In Proceedings of IEEE International Conference on Computational Intelligence and Multimedia Applications, pp. 55–60, 2007.
﹝8﹞S. -T. Hsieh, T. -Y. Sun, C. -C. Liu & S. -J. Tsai , ―Efficient population utilization strategy for particle swarm optimizer,‖ In Proceedings of IEEE Transactions on Systems, Man, and Cybernetics, pp. 444–456, 2009.
﹝9﹞K.E. Parsopoulos & M.N. Vrahatis, ―UPSO: a unified particle swarm scheme,‖ In Lecture series on Computer and Computational Sciences, pp. 868-873, 2004.
﹝10﹞R. Mendes, J. Kennedy & J. Neves, ―The fully informed particle swarm: simpler, maybe better,‖ IEEE Transactions on Evolutionary Computation, pp. 204–210, 2004.
﹝11﹞J.J. Liang & P.N. Suganthan, ―Dynamic multi-swarm particle swarm optimizer,‖ In Proceedings of IEEE on Swarm Intelligence Symposium, pp. 124– 129, 2005.
﹝12﹞M. Pant, T. Radha & V.P. Singh, ―A new particle swarm optimization with quadratic interpolation,‖ In Proceedings of IEEE International Conference on Computational Intelligence and Multimedia Applications, pp. 55-60, 2007.
﹝13﹞Y. –T. Kao, E. Zahara & I. -W. Kao, ―A hybridized approach to data clustering,‖ Expert Systems with Applications, pp. 1754–1762, 2008.
﹝14﹞Z. –J. Lee, ―A novel hybrid algorithm for function approximation,‖ Expert Systems with Applications, pp. 384-390, 2008.
﹝15﹞J. J. Liang, P. N. Suganthan, & K. Deb, ―Novel composition test functions for numerical global optimization,‖ In Proceedings of IEEE on Swarm Intelligence Symposium, pp. 68-75, 2005.
﹝16﹞P. N. Suganthan, N. Hansen, J. J. Liang, K. Deb, Y. -P. Chen, A. Auger & S. Tiwari, ―Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization,‖ Technical report of Nanyang Technological University, 2005.
﹝17﹞R. Salomon, ―Reevaluating genetic algorithm performance under coordinate rotation of benchmark functions,‖ BioSystems, pp. 263-278, 1996.
﹝18﹞葉怡成，類神經網路模式應用與實作，儒林出版社，2003。
﹝19﹞L. A. Zadeh, ―Fuzzy Sets,‖ Information and Control, pp. 338-353, 1965.
﹝20﹞J. Kennedy, R.C. Eberhart & Y. Shi, ―Swarm intelligence,‖ Morgan Kaufmann Publishers, San Francisco, 2001.
﹝21﹞A. E. Eiben & M. Schoenauer, ―Evolutionary computing,‖ Information Processing Letters, pp. 1-6, 2002.
﹝22﹞M. Negnevitsky, ―Artificial Intelligence: A Guide to Intelligent Systems,‖ Wesley Longman Publishing Co., Inc., Boston MA USA, 2001.
﹝23﹞J. H. Holland, ―Adaptation in natural and artificial systems : an introductory analysis with applications to biology, control, and artificial intelligence,‖ Cambridge, Mass : MIT Press, 1992.
﹝24﹞林豐澤，演化式計算下篇：基因演算法以及三種應用實例，智慧科技與應用統計
學報， pp. 29-56。
﹝25﹞王文俊，認識fuzzy，全華出版，第三版，2007。
﹝26﹞X. Cui & T. E. Potok, ―Document Clustering Analysis Based on Hybrid PSO+K-means Algorithm,‖ Journal of Computer Sciences (Special Issue), pp. 27-33, 2005.
﹝27﹞K. Cios, W. Pedrycs & R. Swiniarski, ―Data Mining – Methods for Knowledge Discovery,‖ Kluwer Academic Publishers, 1998.
﹝28﹞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，ICIM2009 第二十屆國際資訊管理學術研討會，pp. 218-227，2009。
﹝29﹞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.
﹝30﹞H. Yoshida, K. Kawata, Y. Fukuyama, S. Takayama & Y. Nakanishi, ‖A Particle Swarm Optimization for Reactive Power,‖ IEEE Transactions on Power Systems, pp. 1232-1239, 2000.
﹝31﹞S.M. GirirajKumar, D. Jayaraj & A.R. Kishan, ―PSO based tuning of a PID controller for a High performance drilling machine,‖ International Journal of Computer Applications, pp. 12-18, 2010.
﹝32﹞X.H. Shi, Y.C. Liang, H.P. Lee, C. Lu & Q.X. Wang, ―Particle swarm
optimization-based algorithms for TSP and generalized TSP,‖ Information Processing Letters 103, pp. 169–176, 2007.
﹝33﹞Y. S. Lee, S. M. Shamsuddin & H. N. Hamed, ―Bounded PSO Vmax Function in Neural Network Learning,‖ Eighth International Conference on Intelligent Systems Design and Applications, pp. 474-479, 2008.
﹝34﹞DW van der Merwe & AP Engelhrecht, ―Data Clustering using Particle Swarm Optimization,‖ Evolutionary Computation, pp. 215-220, 2003.
﹝35﹞A. Abraham, S. Das2 & S. Roy3, ―Swarm Intelligence Algorithms for Data Clustering,‖ In Soft Computing for Knowledge Discovery and Data Mining, pp. 279-313, 2008.
﹝36﹞A.K. Jain, M.N. Murty & P.J. Flynn, ―Data clustering: a review,‖ ACM Computing Surveys, pp. 264-323, 1999.
﹝37﹞M. Omran, A. Salman & A.P. Engelbrecht, ―Image Classification using Particle Swarm Optimization,‖ In Conference on Simulated Evolution and Learning, pp. 370-374, 2002.
﹝38﹞C. Grosan, A. Abraham & M. Chis, ―Swarm Intelligence in Data Mining,‖ Computational Intelligence, pp. 1-20, 2006.
﹝39﹞I.D. Couzin, J. Krause, R. James, G.D. Ruxton & N.R. Franks, ―Collective Memory and Spatial Sorting in Animal Groups,‖ Journal of Theoretical Biology, pp. 1-11, 2002.
﹝40﹞M. R.Anderberg, ―Cluster Analysis far Application,‖ Academic Press, New York, 1973.
﹝41﹞J. Han & M. Kamber, ―Data Mining: Concepts and Techniques,‖ Morgan Kaufmann, 2000.
﹝42﹞Z. -H. Zhan, J. Zhang, Y. Li, & S. -H. Chung, ―Adaptive Particle Swarm Optimization,‖ IEEE Transactions on Systems, Man, and Cybernetics—Part B: Cybernetics, pp. 1362-1381, 2009.
﹝43﹞R.C. Eberhart & Y. Shi, ―Particle Swarm Optimization: Developments, Applications and Resources,‖ Evolutionary Computation, pp. 81-86, 2001. ﹝44﹞W.-D. Chang & S.-P. Shih, ―PID controller design of nonlinear systems using an improved particle swarm optimization approach,‖ Communications in Nonlinear Science and Numerical Simulation, pp. 3632-3639, 2010. ﹝45﹞X. -Z. Li, F. Yu & Y. -B. Wang, ―PSO Algorithm Based Online Self-Tuning of PID Controller,‖ International Conference on Computational Intelligence and Security, pp. 128-132, 2007.
﹝46﹞ L. Fang, P. Chen & S. Liu, ― Particle Swarm Optimization with Simulated Annealing for TSP,‖ Proceedings of the 6th WSEAS Int. Conf. on Artificial Intelligence, Knowledge Engineering and Data Bases, pp. 16-19, 2007.
﹝47﹞Y. Liu, Z. Qin & Z. Shi, ―Hybrid Particle Swarm Optimizer with Line Search,‖ IEEE International Conference on Systems, Man and Cybernetics, pp. 3751-3775, 2004.
﹝48﹞G. Zhang, J. Jiang, Z. Su, M. Qi & H. Fang, ―Searching for overlapping coalitions in multiple virtual organizations,‖ Information Sciences, pp. 3140-3156, 2010.
﹝49﹞J.J. Liang, A.K. Qin, P.N. Suganthan & S. Baskar, ―Comprehensive learning particle swarm optimizer for global optimization of multimodal functions,‖ IEEE Transactions on Evolutionary Computation, pp. 281-296, 2006.
﹝50﹞N. Iwasaki, K. Yasuda, G. Ueno, ―Dynamic parameter tuning of particle swarm optimization,‖ IEEJ Transactions on Electrical and Electronic Engineering 1, pp. 353-363, 2006.
﹝51﹞M. Settles, & T. Soule, ―Breeding Swarms: A GA/PSO Hybrid,‖ In GECCO '05: Proceedings of the 2005 conference on Genetic and evolutionary computation, pp. 161-168, 2005.

指導教授

莊堯棠(Yau-Tang Juang)

審核日期

2011-6-28

推文