博碩士論文 975303014 詳細資訊




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姓名 蔡裕仁(Yu-Jen Tsai)  查詢紙本館藏   畢業系所 通訊工程學系在職專班
論文名稱 粒子群優化演算法應用於企業更新數據網路採購之優化
(Particle Swarm Optimization Algorithm Applied to the Enterprise’s Procurement Optimization of Renewed Data Network)
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摘要(中) 在人類的活動中,存在著大量決策問題,其中所探討的決策過程、決策工具、決策方法,其目的都是為求取符合需要的最佳解。求解過程所依據的不外乎是經驗的累積與工具的運用。隨著人類活動範圍從局域到廣域,彼此的連結度從鬆散到緊密,所遇到的問題越來越複雜化,以往所依據的經驗與工具,似乎無法較有效率的處理現在所遇到的龐大問題。
學者們從生物群體行為系統中探討其潛在智能,加上計算機技術的快速精進,提出了一系列藉由生物智能觀念的問題求解。而‘粒子群優化演算法’就是生物體經由自身經驗與群體經驗所計算的求解方法,它具有參數設定少、搜尋速度快和可行性高的優點。所以目前已被學者們廣泛發表相關之實務應用。
此論文內容就是藉由‘粒子群優化演算法’的優點應用於企業網路採購時的優化問題求解。希望能提供一種更簡潔、更有效、更優化的參考依據,利於專案執行者於專案決策時,多了一種決策過程中更有效率的決策工具。
摘要(英) In human activities a lot of decision-making issues exist, among them, the decision-making process, tool, and approach, all these need to acquire the best solution to meet their requirements. Inevitably, the solution-seeking process focuses on the experience-accumulation and tool-application. Following the activity scope of human-from narrow to wide; and their mutual link-from loose to tight, the facing problems have become more and more complex. Earlier experiences or tools seem not possible to deal with existing big problem efficiently.
Scholars explored the potential intelligence from the system of biological colony behavior, fueled by the fast development of calculator technology, to present a series of solution derived from the biological intelligence concept. However, the particle swarm optimization algorithm (PSO) is a solution formula through the calculation on self experience and colony experiences of creatures. Its merits include few parameters setting, prompt sourcing speed, and high feasibility. Consequently, many scholars had massively announced the related applications in practice.
This thesis adopts the merits of PSO to deal with the optimization problem of enterprise’s network procurement, in order to provide references of more simple, effective, and optimal standards. Thereby it offers a more efficient tool for the project-performer in the course of decision making.
關鍵字(中) ★ 粒子群優化演算法
★ 採購方案
★ 網路規劃
★ 決策工具
關鍵字(英) ★ Particle Swarm Optimization Algorithm (PSO)
★ Procurement scheme
★ Decision-making tool
★ Network Planning
論文目次 摘 要 i
Abstract ii
誌 謝 iii
目 錄 iv
圖 目 錄 List of Figures v
表 目 錄 List of Tables vi
第一章 緒 論 1
1-1 研究背景與動機 1
1-2 論文架構 3
第二章 群智能粒子群優化演算法 4
2-1 群智能概述 4
2-2 粒子群優化演算法的原理概述 6
2-3 粒子群優化演算法的數學描述 7
2-4 慣性權重粒子群優化演算法 12
2-5 粒子群優化演算法的參數選擇 14
第三章 更新數據網路採購問題之優化 16
3-1 企業網路問題概述與規劃 16
3-1-1 公司網路規劃方向 16
3-1-2公司網路現況與缺失 17
3-1-3 公司評估改善網路系統概述 21
3-1-4公司預算核定 26
3-2 網路服務業者與設備商報價彙總 27
3-3 品項代碼定義與品項相關金額限制 32
3-3-1 購買品項代碼定義與品項金額範圍 32
3-3-2 購買品項需求限制 34
第四章 限制預算下利用PSO求解各品項的最大值 36
4-1 問題定義與參數設定 36
4-2 隨機產生起始解 37
4-3 定義適應值(Fitness value)函數 39
4-4 進行V向量的更新 40
4-5 檢查是否達到停止條件 40
4-6 參數選擇與實測結果 41
4-6-1 實測求解測試 41
4-6-2 實測求解結果 43
4-7 企業網路採購優化計算結果 44
第五章 結論 45
參 考 文 獻 47
參考文獻 [1] Jan A. Snyman (2005). Practical Mathematical Optimization: An Introduction to Basic Optimization Theory and Classical and New Gradient-Based Algorithms. Springer Publishing.
[2] S.S. Rao (1984). Optimization Theory and Applications, Second Edition, Wiley Eastern Limited, New Delhi.
[3] Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein. Introduction to Algorithms, Second Edition. MIT Press and McGraw-Hill, 2001. Section 29.3: The simplex algorithm, pp.790–804.
[4] Hopfield, J. and Tank, D. (1985). Neural computation of decisions in optimization problems. Biological Cybernetics, 52:141–152.
[5] Hopfield J.J. and Tank D.W., "Computing with neural circuits: a model", Science, vol.233, 1986, pp. 625-633.
[6] Goldberg, D.E. (1989) Genetic algorithms in search optimization and machine learning. Addison-Wesley, MA, USA.
[7] Rudolph, G., (1994): "Convergence analysis of canonical genetic algorithms", IEEE Transactions on Neural Networks, 5, pp. 96–101.
[8] Kirkpatrick, S.; Gelatt, C. D.; Vecchi, M. P. (1983). "Optimization by Simulated Annealing". Science, Vol. 220, pp. 671–680.
[9] Kirkpatrick, S. (1984), "Optimization by Simulated Annealing: Quantitative Studies", Journal of Statistical Physics, Vol. 34, pp. 975–986.
[10] G. Beni and J. Wang (1989), "Swarm intelligence in cellular robotics systems", Proceedings of NATO Advanced Workshop on Robots and Biological System, pp. 703–712.
[11] E. Bonabeau, M. Dorigo, G. Theraulaz (1999), Swarm intelligence: from natural to artificial systems.
[12] Kennedy, J.; Eberhart, R. (1995). "Particle Swarm Optimization". Proceedings of IEEE International Conference on Neural Networks. IV. pp. 1942–1948.
[13] Shi, Y.; Eberhart, R.C. (1998). "A modified particle swarm optimizer". Proceedings of IEEE International Conference on Evolutionary Computation. pp. 69–73.
[14] Shi, Y.; Eberhart, R.C. (1999). "Empirical study of particle swarm optimization". Proceedings of the Congress on Evolutionary Computation, pp. 1945–1950.
[15] Shi, Y.; Eberhart, R.C. (1998). "Parameter selection in particle swarm optimization". Proceedings of Evolutionary Programming VII (EP98). pp. 591–600.
[16] Kennedy, J. (1997). "The particle swarm: social adaptation of knowledge". Proceedings of IEEE International Conference on Evolutionary Computation. pp. 303–308.
指導教授 賀嘉律(Chia-Lu Ho) 審核日期 2011-7-30
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