[Back to Results | New Search]

Student Number 965303019 Author Tsung-Hsun Wu(吳宗勳) Author's Email Address No Public. Statistics This thesis had been viewed 1454 times. Download 0 times. Department Executive Master of Communication Engineering Year 2008 Semester 2 Degree Master Type of Document Master's Thesis Language zh-TW.Big5 Chinese Title Particle Swarm Optimization Algorithm Applied to the Telecom Industry for Total Solution Partner Selection and Project Scheduling Optimization Date of Defense 2009-06-26 Page Count 83 Keyword Fuzzy Decision JSP Partner PSO Swarm Intelligence Total Solution Abstract The Telecommunications market has many competitor in Taiwan, result in profits decline. The carrier in order to provide customized services for “Key Account and Small Medium Enterprise Customers”, these carrier create a “one-stop-shopping” Selling model, it also call “Total Solution”. Total Solution must integrate a number of business partners for related implement services, and they can provide the best solution to the customer. The purpose of this thesis that can select best partners after carrier obtain customer official orders, and focus the construction project improve a shortest implement time and the lowest labor costs, and according to the project scheduling to steps by steps implement and build these engineering.

The Thesis is based on the concept of Swarm Intelligence, it call “Particle Swarm Optimization (PSO)” Algorithm. PSO Algorithm contains rapid convergence characteristics. In recent years, it is a swarm intelligence algorithm for applied to solve some optimization question. PSO emphasis on inter-particle communication and it has some advantage, for example, low parameters setting, fast to search and feasibility of high-speed, etc. Now it has many scholars that has been published with PSO related improvement algorithms associated with such a practical application.

The thesis was divided into three parts; the first part of the first to introduce the concept of swarm intelligence and then introduce more popular optimization algorithm at present in several academic organization, and based on PSO optimization algorithm to do a simple comparison algorithm with others. The second part, we will focus on basic concept of the PSO and all kind of the improvement PSO algorithm description its further concepts and variety. The third part is the combination of the use of improved PSO concept and fuzzy decision-making, according to Total Solution executing partner election and total cost ownership optimization after the carrier get the orders, and use values of multi status to proof PSO/FD can be made more efficient partner selection of the optimal solution to the problem, and then we used PSO combine “Job-shop Scheduling Problem(JSP)” strategies for the project during construction period, to optimize the scheduling in order to shortest the construction period, and it can achieve cost savings. Final chapter we can propose some conclusions and face to further improvement PSO, and propose future study and some related research suggestions.Table of Content 摘 要

Abstract

誌 謝

目 錄

圖 目 錄 List of Figures

表 目 錄 List of Tables

第一章 緒 論1

1-1 研究背景與動機1

1-2 論文架構與流程3

第二章 數種優化演算法之介紹與比較5

2-1 群體智能之概念5

2-2 優化演算法介紹7

2-2-1 基因演算法7

2-2-2 模擬退火演算法10

2-2-3 螞蟻群優化演算法12

2-3 數種演算法之比較16

2-3-1 PSO演算法初探16

2-3-2 PSO與基因演算法(GA)的比較17

2-3-3 PSO與模擬退火法(SA)之比較19

第三章 粒子群優化演算法及進階研究20

3-1 粒子群優化演算法20

3-2 PSO之改進演算法之發展27

3-3 PSO整合模糊決策30

3-4 PSO整合JSP31

第四章 PSO應用於電信業解決方案之優化35

4-1 台灣電信產業近年之發展35

4-2 電信業解決方案(Total Solution)之銷售模式38

4-3 選商問題之優化42

4-3-1 問題假設與定義42

4-3-2 實測結果及分析52

4-4 專案排程問題之優化59

4-4-1 問題定義及模型59

4-4-2 實測結果及分析60

第五章 結論與未來展望64

5-1 結論64

5-2 未來展望66

參 考 文 獻67Reference [1] Angeline, P.J., “Using selection to improve particle swarm optimization”, IEEE World Congress on Computational Intelligence, pp. 84-89, 1998.

[2] C. A. C. Coello and M. S. Lechuga, “MOPSO: a proposal for multiple objective particle swarm optimization”, Proceedings of the IEEE Congress on Evolutionary Computation, Honolulu, Hawaii, USA, 2002.

[3] Eberhart, R.C. and Shi, Y. “Comparison between genetic algorithms and particle swarm optimization”, 1998 Annual Conference on Evolutionary Programming, 1998.

[4] Eberhart, R.C. and Kennedy, J., “A new optimizer using particle swarm theory”, Proc. Sixth International Symposium on Micro Machine and Human Science, pp.39-43, 1995.

[5] Eberhart, R.C. and Shi, Y., “Particle Swarm Optimization: Developments, Applications and Resources”, Proc. IEEE Int. Conf. On Evolutionary Computation, Vol.1, pp. 81-86, 2001.

[6] Esmin, A.A.A., A.R. Aoki and G. Lambert-Torres, “Particle Swarm Optimization for Fuzzy Membership Functions Optimization”, IEEE International Conference, Vol. 3, 2002.

[7] Fukuyama, Y., and Yoshida, H., “A Particle Swarm Optimization for Reactive Power and Voltage Control in Electric Power Systems”, Proc. IEEE Congress Evolutionary Computation, Vol. 1, pp. 87-93, 2001.

[8] Gen, M., Tsujimura Y. and Kubota, E., “Solving Job-Shop Scheduling Problems by Genetic Algorithm”, IEEE International Conference on, Vol. 2, pp. 1577-1582, 1994.

[9] H.Yoshida, K. Kawata, Y.Fukuyama, and Y .Nakanishi, “A particles warm optimization for reactive power and voltage control considering voltage stability”, Proceedings of the International Conference on Intelligent System Application to Power System, Riode Janeiro, Brazil, pp. 117-121, 1999.

[10] H. Lu, “Dynamic population strategy assisted particle swarm optimization in multi objective evolutionary algorithm design”, IEEE NNS Student Research Grants, 2002-Final Reports, 2003.

[11] Hu, X., R.C. Eberhart and Y. Shi “Swarm intelligence for permutation optimization: a case study of n-queens problem”, Swarm Intelligence Symposium, The Proceedings of IEEE on SIS, pp.243-246, 2003.

[12] John. H. Holland, “Adaptation in Natural and Artificial Systems”, MIT Press, 1975.

[13] J.J. Hopfield et al, “Computing with neural circuits: A model”, Science 233 625-33, 1986.

[14] J. Kennedy and R .C. Eberhart, “Particle Swarm Optimization”, Proceedings of IEEE International Conference on Neural Networks, 1995 Perth, Australia:1942-1948.

[15] J. Kennedy and R. C. Eberhart, “A discrete binary version of the particle swarm algorithm”, Proceedings of the World Multi conference on Systemic, Cybernetics and Informatics 1997, Piscataway, New Jersey, USA.:4 104-4109, 1997.

[16] J. Kennedy, R. C. Eberhart and Y. Shi, “Swarm Intelligence”, San Francisco: Morgan Kaufmann, 2001.

[17] J. Kennedy, R. C. Eberhart, “Particle Swarm Optimization”, Proc, IEEE International Conf. Neural Networks, Vol. 4, pp. 1942-1948, 1995.

[18] J. Kennedy, “Stereotyping: Improving Particle Swarm Performance with Cluster Analysis”, Proc. IEEE Int. Conf. on Evolutionary Computation, Vol. 2 pp. 1507-1512, 2000.

[19] Jyh-Shing Roger Jang, Chuen-Tsai Sun, Eiji Mizutani, “Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence”, Prentice Hall, 1996.

[20] K. E. Parsopoulos and M. N Vrahatis, “Particle swami optimization method in multi objective problems”, Proceedings of the ACM Symposium on Applied Computing 2002, Madrid, Spain: 6 03-607, 2002.

[21] Kirkpatrick, S., Gelatt, C. D. and Vechi, M. P., “Optimization by Simulated Annealing”, Science (13:80), pp. 671-780, 1983.

[22] Kirkpatrick, S., “Optimization by Simulated Annealing: Quantitative Studies”, Journal of Static Physics (34), pp. 975-986, 1984.

[23] L. A. Zadeh, “Fuzzy sets”, Information and Control 8, pp. 338-353, 1965.

[24] L. A. Zadeh, “Outline of a new approach to the analysis of complex systems and decision processes”, IEEE Trans. on Syst., Man and Clyburn, SMC-3, pp. 28-44, 1973.

[25] Maria P, Joseph Christie Levary R., “Virtual corporations: recipe for success”, Industrial Management, Chapter (6-8), 7-11, 1998.

[26] N. Metropolis et.al., “Equation of state calculation by fast computing machines”, Journal of Chemical Physics 21 1087-92, 1953.

[27] Ponnambalam, S.G., Aravindan, P. and Rajesh, S.V., “A tabu search algorithm for job shop scheduling”, International Journal of Advanced Manufacturing Technology, Vol. 16, pp.765-771, 2000.

[28] Proc congress on evolutionary computation [C] IEEE service center, Piscataway, NJ, Seoul, Korea, pp. 81-86, 2001.

[29] R. C. Eberhart and J. Kennedy, “A new optimizer using particle swarm theory”, Proceedings of the Sixth International Symposium on Micro machine and Human Science, Nagoya, Japan, pp. 39-43, 1995.

[30] Robert R Korfhage, “Information Storage and Retrieval”, Wiley Computer Publishing, New York, pp. 196-199, 1997.

[31] Salerno, J, “Using the particle swarm optimization technique to train a recurrent neural model”, The Proceedings of the Ninth IEEE International Conference on Tools with Artificial Intelligence, pp. 45-49, 1997.

[32] Shi, Y. and Eberhart, R.C., “Parameter Selection in Particle Swarm Optimization”, 7th Int. Conf. on Evolutionary Programming, Vol. 1447, pp. 591-600, 1998.

[33] Shi, Y. and Eberhart, R.C., “A Modified Particle Swarm Optimizer”, IEEE Int. Conf. on Evolutionary Programming, pp. 69-73, 1998.

[34] Shi, Y. and Eberhart, R.C, “Empirical Study of Particle Swarm Optimization”, Proceedings of the Evolutionary Computation 1999 Congress, Vol. 3, pp. 1945-1950, 1999.

[35] Wang, K.P., L. Huang, C.G. Zhou and W. Pang, “Particle Swarm Optimization for Traveling Salesman Problem”, The Proceedings of the Second International Conference on Machine Learning and Cybernetics vol. 3, pp. 1583-1585, 2003.

[36] X .Huand R .C .Eberhart, “Multi objective optimization using dynamic neighborhood, particle swarm optimization”, Proceedings of the IEEE Congress on Evolutionary Computation, Honolulu, Hawaii, USA: 1677-1681, 2002.

[37] Xiaohui, Hu, Eberhart, R.C. and Shi, Y., “Engineering Optimization with Particle Swarm”, Swarm Intelligence Symposium, pp.53-57, 2003.

[38] Yin, P.Y., “A Discrete Particle Swarm Algorithm for Optimal Polygonal Approximation of Digital Curves, Journal of Visual Communication and Image Representation”, Vol. 15, pp 241-260, 2004.

[39] Z. Michalewicz, “Genetic Algorithms + Data Structures = Evolution Programs”, (3rd. Ed.) Springer Verlag, 1996.

[40]朱淑娟, “使用粒子聚積法及蟻群最佳化混合分群方法之應用”, 行政院國家科學委員會專題研究計畫 成果報告,pp 2-12, 2005

[41]李世炳、鄒忠毅, “簡介導引模擬退火法及其應用”, 物理雙月刊（廿四卷二期）, pp 307-310, 2002.

[42]李宜原, “改良式遺傳演算法於零工式生產排程系統之應用”, 海洋大學系統工程暨造船學系研究所, 碩士論文, 2004.

[43]吳萬成, “以粒子族群最佳化進行倒傳遞類神經網路參數最佳化與屬性篩選之研究”, 碩士論文, 華梵大學 資訊管理系, pp 21-26, 2006.

[44] 林信成、彭啟峰, “Oh！Fuzzy 模糊理論剖析”, 第三波出版社, 1994.

[45]孫碧娟, “買賣關係中信任之前置因素與結果－台灣電信產業之實證研究”, 行銷評論, 第3卷, 第3期, pp 333-348, 2006.

[46]陳建良, “排程概述”, 機械工業雜誌, 1995.

[47]陳譽升, “應用蟻群演算法於半導體晶圓廠之設施規劃問題”, 碩士論文, 元智大學 工業工程管理所, pp 13-22, 2005.

[48]梁添富, “模糊多目標線性規劃在專案趕工決策之應用”, 修平技術學院工業管理系, pp1-13, 2003.

[49]辜文元, “多目標直交粒子群最佳化演算法”, 逢甲大學資關所碩士論文, pp17-25, 2005.

[50] 曾建潮、介婧、崔志華, “微粒群算法”, 北京, 科學出版社, 2004.

[51] 劉清祥, “粒子群演算法於結構設計及零工式排程之應用”, 海洋大學系統工程暨造船學系研究所, 碩士論文, 2005.

[52]廖慶榮, “作業研究” ,華泰書局, pp8-24, 2004.

[53]蔡清欉， “以粒子群最佳化為基礎之電腦遊戲角色設計之研究”, 東海大學資訊工程與科學研究所, 碩士論文, 2003.

[54]鄭毅, 吳斌, “由鳥群和螞蟻想到的一基於主體的仿真和群集智能的研究”, 微電腦世界, pp7-13, 2001.Advisor Chia-Lo Ho(賀嘉律)

Files disapprove authorization

965303019.pdf Date of Submission 2009-07-07

Our service phone is (03)422-7151 Ext. 57407,E-mail is also welcomed.