博碩士論文 953202051 詳細資訊




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姓名 劉向邦(Xiang-Bang Liu)  查詢紙本館藏   畢業系所 土木工程學系
論文名稱 以和諧搜尋演算法為基礎之混合式全域搜尋演算法求解含凹形節線成本最小成本轉運問題之研究
(Hybrid Global Search Algorithm Based on Harmony Search for Concave Cost Minimum Cost Network Flow Problems)
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摘要(中) 傳統在最小成本轉運問題定式下,常以線性方式來定義運送成本,藉以簡化問題的複雜度。在實務上,貨物運送的單位成本常隨數量的增加而遞減,其成本函數曲線為凹形。以往有不少含凹形成本節線之研究,但侷限於不同之特殊網路且方法屬傳統區域搜尋法或傳統啟發解法,近期雖有學者開始以新近鄰近搜尋法求解簡化的運輸問題,以達到較大範圍的搜尋方式,期能找到較優於傳統啟發解法之解,卻忽略運輸網路常見的轉運問題。因此近期有學者發展類遺傳演算法、類螞蟻族群演算法及粒子群演算法以求解含凹形節線成本最小成本網路流動問題。另外,新近的和諧搜尋演算法目前在各領域的問題求解上效果頗佳,甚至有發現較遺傳演算法為佳,但尚未發現有應用於含凹形節線成本最小成本網路流動問題,緣此,本研究針對含凹形節線成本之最小成本網路流動問題,以和諧搜尋演算法之搜尋概念為基礎,並結合粒子群演算法、螞蟻族群演算法、門檻值接受法與凹形成本網路啟發解法之特點,以節線及路徑為基礎發展一混合式全域搜尋法,期能有效的求解含凹形節線成本之最小成本網路流動問題。
在求解方法上,本研究以和諧搜尋演算法為基礎,利用和諧搜尋演算法中以和弦記憶空間為母體之選擇機制之全域搜尋法與調音機制之區域搜尋法重新組成新可行解,並加入PSO速度更新策略、ACS費洛蒙更新策略、TA與凹形成本網路啟發解法之演算特色,針對凹形成本網路流動問題之特性,發展一套適合凹形節線成本轉運問題之類和諧搜尋演算法。最後,為測試本研究演算法在不同規模及參數的網路問題之求解績效,本研究設計一隨機網路產生器,產生大量隨機網路,在個人電腦上以C++語言撰寫所有相關的電腦程式,並測試新近發展之遺傳演算法、門檻值接受法、大洪水法、類螞蟻族群演算法及粒子群演算法,以評估本研究演算法之求解績效,進而提供實務界求解此類實際的網路運送問題之參考。測試結果顯示,本研究演算法求解品質良好。
摘要(英) Traditionally, the minimum cost transshipment problems were simplified as linear cost problems in order to reduce problem complexity. In practice, the unit cost for transporting freight usually decreases as the amount of freight increases. Hence, in actual operations the transportation cost function can usually be formulated as a concave cost function. Great efforts have been devoted to the development of solution algorithms. However, they were confined to specical transportation networks. Besides, their methods were focused on local search algorithms or traditional heuristics. Recently, researchers began to use advanced neighborhood search algorithms to solve concave cost bi-partite transportation network problems to enlarge search area and find near-optimal solutions. This type of research, however, neglected flow transfers in transportation networks. Recently, there has been research adopting the genetic algorithm (GA), the ant colony system algorithm (ACS) and the particle swarm optimization algorithm (PSO) for solving concave cost network problems, and obtaining better solutions than some neighborhood search algorithms do. The harmony search (HS), a global search algorithm, has led to good results in many applications. In some applications, HS was even more effective than GA. Since there has not yet been any research applying HS to minimum concave cost network flow problems, we employ HS, coupled with the techniques of PSO, ACS and TA, to develop one global search algorithms for efficiently solving minimum concave cost network flow problems.
In the solution method, we take the harmony search as the foundation, use a global search which is harmony memory consideration and a local search which is Pitch Adjusting to compose the new feasible solution, we also join the velocity update rules in PSO, the pheromone update rules in ACS, TA and the concave cost initial solution algorithm, we develop a analogous harmony search which is fitting the minimum cost transshipment problems with concave costs. Finally, to evaluate our algorithms we designed a network generator to create a sufficient number of problem instances. The C++ computer language was used to code all the necessary programs and the test was performed on personal computers. To evaluate our algorithm, we also tested the recently designed TA, GDA, GA, ACS and PSO that solve minimum concave cost network flow problems. The results show that the developed algorithms performed well in the tests.
關鍵字(中) ★ 和諧搜尋演算法
★ 凹形節線成本
★ 區域搜尋
★ 全域搜尋
★ 最小成本網路流動問題
關鍵字(英) ★ concave arc cost
★ global search
★ local search
★ minimum cost network flow problem
★ hamony search
論文目次 摘要............................................................................................................................... I
ABSTRACT.................................................................................................................. II
誌謝…………………………………………………………………………………..III
目錄…………………………………………………………………………………..IV
圖目錄………………………………………………………………………………..VI
表目錄……………………………………………………………………………… VII
第一章 緒論...............................................................................................................1
1.1 研究背景與動機.............................................................................................1
1.2 研究目的與範圍.............................................................................................2
1.3 研究方法與流程.............................................................................................3
第二章 文獻回顧.......................................................................................................4
2.1 凹形成本網路流動問題.................................................................................4
2.2 鄰近搜尋法.....................................................................................................5
2.3 和諧搜尋演算法.............................................................................................9
2.4 全域式演算法...............................................................................................11
2.5 文獻評析.......................................................................................................16
第三章 問題描述與求解演算法設計.....................................................................17
3.1 問題定式及特性...........................................................................................17
3.2 求解演算法設計...........................................................................................19
3.2.1 演算法之步驟....................................................................................19
3.2.2 初始解產生策略................................................................................19
3.2.3 可行解產生策略................................................................................20
3.2.4 PSO 速度更新公式............................................................................23
3.2.5 PSO 加入節線策略............................................................................24
3.2.6 供需節點對選擇策略........................................................................24
3.2.7 虛擬節線成本產生策略....................................................................25
3.2.8 狀態轉移策略....................................................................................25
3.2.9 費洛蒙更新策略................................................................................26
3.2.10 變數調整策略..................................................................................27
3.2.11 門檻值觀念......................................................................................27
3.2.12 鄰近搜尋法之改善策略..................................................................27
3.2.13 演算法終止機制..............................................................................28
3.2.14 小結..................................................................................................28
第四章 實證分析.....................................................................................................32
4.1 網路產生器設計...........................................................................................32
4.1.1 隨機網路產生法................................................................................32
4.1.2 供給(需求)節點與供給(需求)量的隨機產生法..............................33
4.2 AHS 求解策略測試.......................................................................................34
4.2.1 初始解相關參數................................................................................36
4.2.2 和諧搜尋演算法相關參數................................................................42
4.2.3 PSO 相關參數....................................................................................50
4.2.4 ACS 相關參數....................................................................................58
4.2.5 TA 相關參數.......................................................................................63
4.2.6 鄰近搜尋法搜尋範圍........................................................................67
4.2.7 最佳參數組合....................................................................................68
4.2.8 初始解與最終解之相關性................................................................70
4.2.9 AHS 收斂趨勢....................................................................................71
4.2.10 小結..................................................................................................74
4.3 AHS 與APSO、AACS、GA 與各區域搜尋法之求解績效比較..............75
第五章 結論與建議………………………………………………………...……...80
5.1 結論...............................................................................................................80
5.2 貢獻...............................................................................................................81
5.3 建議...............................................................................................................82
參考文獻.....................................................................................................................83
附錄一 AHS 各方案參數值......................................................................................89
附錄二 AHS 各方案測試詳細結果..........................................................................93
附錄三 GA、AACS、APSO 與各區域搜尋法輸入參數值.................................. 111
附錄四 各型網路目前最佳解................................................................................. 113
參考文獻 1. 王政嵐,「類螞蟻族群演算法於求解含凹形節線成本最小成本轉運問題之研究」,中央大學土木工程研究所碩士論文(2005)。
2. 王珮珮,「使用粒子群最佳化進行分散式系統之最佳工作分配」,暨南國際大學資訊管理學系碩士論文(2004)。
3. 田佳芸,「變動鄰域搜尋法於雙目標平行機台排程問題之研究」,元智大學工業工程與管理學系碩士論文(2006)。
4. 朱文正,「考量旅行時間可靠度之車輛途程問題─螞蟻族群演算法之應用」,國立交通大學交通運輸研究所碩士論文(2002)。
5. 李旺蒼,「以粒子群最佳化為基礎之混合式全域搜尋演算法求解含凹形節線成本最小成本轉運問題之研究」,中央大學土木工程研究所碩士論文(2006)。
6. 李亮、遲世春、褚雪松,「基於修復策略的改進和聲搜索演算法求解土坡非圓臨界滑動面」,中國岩土力學,第27卷,第10期(2006)。
7. 李亮、遲世春、鄭榕明、林皋,「一種新型遺傳演算法及其在土坡任意滑動面確定中的應用」,中國水利學報,第38卷,第2期(2007)。
8. 李岳倫,「以螞蟻識別系統進行零件分群」,大同大學資訊經營所碩士論文(2004)。
9. 林依潔,「整合模糊理論與螞蟻演算法於含時窗限制之車輛途程問題」,國立台北科技大學生產系統工程與管理研究所碩士論文(2002)。
10. 段建帆,「支援向量機之最佳化參數與屬性篩選之分散式資料探勘系統—以粒子群最佳化演算法為基礎 」,華梵大學資訊管理學系碩士論文(2004)。
11. 胡曉輝,「基於PSO演算法的神經網絡集成構造方法」,浙江大學學報 (2004)。
12. 郭禎祥,「直交調和搜尋最佳化演算法」,東海大學工業工程與經營資訊學系碩士論文(2006)。
13. 陳建榮,「含凹形節線成本最小成本網路流動問題之全域搜尋演算法研究」,中央大學土木工程研究所碩士論文(2002)。
14. 陳怡靜,「變動鄰域搜尋法於串並聯系統複置配置問題之研究」,元智大學工業工程與管理學系碩士論文(2004)。
15. 梁韵嘉、羅敏華、簡士超、康添啟,「變動鄰域搜尋法求解越野賽跑問題」, 第四屆台灣作業研究學會學術研討會暨2007年作業研究理論與實務學術研討會論文集(2007)。
16. 程哲廞,「變動鄰域搜尋法求解共同到期日之單機加權提早延遲問題」,國立臺灣科技大學工業管理系碩士論文(2005)。
17. 詹達穎,「模擬鍛鍊法求解車輛排程之探討」,中華民國運輸學會第九屆論文研討會論文集,第185-192頁(1994)。
18. 鄭佳琳,「結合門檻接受法與螞蟻演算法於求解車輛路線問題之研究」,中華大學運輸科技與物流管理所碩士論文(2006)。
19. 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碩士論文(2004)。
20. 劉德誠,「以PSO為基礎的臉部偵測系統」,長庚大學資訊管理研究所碩士論文(2005)。
21. 韓復華、林修竹,「TA與GDA巨集啟發式法在VRPTW問題上之應用」,中華民國第四屆運輸網路研討會,第83-92頁(1999)。
22. 韓復華、卓裕仁,「門檻接受法、成本擾動法與搜尋空間平滑法在車輛路線問題之應用研究與比較分析」,運輸學刊,第九卷,第三期,第103-129頁(1996)。
23. 韓復華、陳國清、卓裕仁,「成本擾動法在TSP問題之應用」,中華民國第二屆運輸網路研討會論文集,第283-292頁(1997)。
24. 韓復華、楊智凱,「門檻接受法在TSP問題上之應用」,運輸計劃季刊,第二十五卷,第二期,第163-188頁(1996)。
25. 韓復華、楊智凱、卓裕仁,「應用門檻接受法求解車輛路線問題之研究」,運輸計畫季刊,第二十六卷,第二期,第253-280頁(1997)。
26. 顏上堯、周容昌、李其灃,「交通建設計畫評選模式及其解法之研究─以中小型交通建設計畫的評選為例」,運輸計畫季刊,第三十一卷,第一期(2002)。
27. 蘇昱豪,「具隨機粒子與微調機制式粒子群最佳化於多極值函數問題之研究」,國立臺灣科技大學機械工程系碩士論文(2005)。
28. Abuali, F. N., Wainwright, R. L. and Schoenefeld, D. A., “Determinant Factorization: ANew Encoding Scheme for Spanning Trees Applied to the Probabilistic Minimum Spanning Tree Problem,” Proceedings of The Sixth International Conference on GeneticAlgorithms, pp. 470-477 (1995).
29. Ahuja, R. K., Maganti, T. L. and Orlin, J. B., Network Flows, Theory, Algorithms, and Applications, Prentice Hall, Englewood Cliffs (1993).
30. Alfa, A. S., Heragu, S. S. and Chen, M. “A 3-opt Based Simulated Annealing Algorithm for Vehicle Routing Problem,” Computers and Industrial Engineering, Vol. 21, pp. 635-639 (1991).
31. Alistair S., “Improved Bounds for Mixing Rates of Markov Chains and Multicommodity Flow,” Springer Lecture Notes in Computer Science Vol. 583 (1992).
32. Amiri, A. and Pirkul, H., “New Formulation and Relaxation to Solve A Concave Cost Network Flow Problem,” Journal of the Operational Research Society, Vol. 48, pp. 278-287 (1997).
33. Balakrishnan, A. and Graves S. C., “A Composite Algorithm for a Concave-Cost Network Flow Problem,” Networks, Vol. 19, pp. 175-202 (1989).
34. Bielli, M., Caramia M. and Carotenuto P., “Genetic Algorithms in Bus Network Optimization,” Transportation Research, Part C 10, pp. 19-34 (2002).
35. Blumenfeld, D. E., Burns, L. D., Diltz, J. D. and Daganzo, C. F., “Analyzing Trade-offs Between Transportation, Inventory, and Production Costs on Freight Network,” Transportation Research, Vol. 19B, pp. 361-380 (1985).
36. Booker, L. B., Goldberg, D. E. and Holland, J. H., “Classifier Systems and Genetic Algorithms,” Technical Report, No. 8 (1987).
37. Burke, E.K., De Causmaecker, P., Petrovic, S., Berghe G.V., “Variable Neighbourhood Search for Nurse Rostering Problems,” Computer Decision-Making, Kluwer, pp. 153-172 (2003).
38. Charon, I. and Hurdy, O., “The Noising Method: A New Method for Combinatorial Optimization,” Operations Research Letters, Vol. 14, pp. 133-137 (1993).
39. Craig W. R., “Flocks, Herds, and Schools: A Distributed Behavioral Model,” Computer Graphics, pp. 25-34 (1987).
40. Davidovic, T., Hansen, P. and Mladenovic, N., “Variable Neighborhood Search for Multiprocessor Scheduling Problem with Communication Delays,” Metaheuristics International Conference, Poroto, Portugal, pp. 737-741 (2001).
41. Davis, L., “Adapting Operator Probabilities in Genetic Algorithms,” The Third International Conference on Genetic Algorithms, pp. 61-69 (1989).
42. Deneubourg, J. L., S. Goss, N., A. Sendova-Franks, C. Detrain and L. Chretien, “The Dynamics of Collective Sorting Robot-like Ants and Ant-like Robots,” Proc. Of the 1st Conf. on Sim. of Adaptive Behavior, pp. 356-363 (1991).
43. Dorigo, M., Maniezzo, V. and Colorni, A., “The Ant System: An AutocatalyticOptimizing Process,” Technical Report No. 91-016 Revised, Politecnico di Milano, Italy (1991).
44. Dorigo, M. and Gambardella, L. M., “A Study of Some Properties of Ant-Q,” Proceedings of PPSN IV-Fourth International Conference on Parallel Problem Solving From Nature, September 22-27, 1996, Berlin, Germany, Berlin: Springer-Verlag, 656Ð665 (1996). (Also Tecnical Report TR/IRIDIA/1996-4, IRIDIA, Université Libre de Bruxelles.)
45. Dorigo, M. and L. M. Gambardella, “Ant Colonies for the Traveling Salesman Problem,” BioSystems, Vol. 43: pp. 73-81 (1997).
46. Drummond, L.M.A., Vianna, L.S., Silva, M.B. and Ochi, L.S., “Distribution Parallel Metaheuristics Based on GRASP and VNS for Solving the Traveling Purchaser Problem,” Proceedings of the 9th International Conference on Parallel and Distributed System, pp. 257-263 (2002).
47. Dueck, G., “New Optimization Heuristics: The Great Deluge Algorithm and the Record-to-Record Travel,” Journal of Computational Physics, Vol. 104, pp. 86-92 (1993).
48. Dueck, G. and Scheuer, T., “Threshold Accepting: A General Purpose Optimization Algorithm Appearing Superior to Simulated Annealing,” Journal of Computational Physics, Vol. 90, pp.161-175 (1990).
49. Dukwon, K. and Panos, M., “Dynamic Slope Scaling and Trust Interval Techniques for Solving Concave Piecewise Linear Network Flow Problems,” Networks, Vol. 35, pp. 216-222 (2000).
50. Eberhart, R. C. and Kennedy, J. “A New Optimizer Using Particle Swarm Theory. Proceedings of the Sixth International Symposium on Micro Machine and Human Science,” pp. 39-43. IEEE service center, Piscataway, NJ, Nagoya, Japan (1995).
51. Eberhart, R. C. and Kennedy, J. , “Particle Swarm Optimization”, In proceedings of IEEE International Conference on Neural Networks, Vol. 4, pp.1942-1948 (1995)
52. Eberhart, R. C. and Shi, Y. “Comparison Between Genetic Algorithms and Particle Swarm Optimization,” Evolutionary programming vii: proc. 7th ann. conf. on evolutionary conf., Springer-Verlag, Berlin, San Diego CA (1998).
53. Eberhart, R. C. and Shi, Y. “Particle Swarm Optimization: Developments, Applications and Resources,” Proc. congress on evolutionary computation 2001 IEEE service center, Piscataway, NJ., Seoul, Korea. (2001).
54. Frans van den B. and Engelbrecht A. P. “A Cooperative Approach to Particle Swarm Optimization,” IEEE Trans on Evolutionary Computation, pp.225-239 (2004).
55. Gallo, G. and Sandi, C., “Adjacent Extreme Flows and Application to Min Concave Cost Flow Problems,” Networks, Vol. 9, pp. 95-121 (1979).
56. Gallo, G., Sandi C. and Sodini, C., “An Algorithm for the Min Concave Cost Flow Problem,” European Journal of Operation Research, Vol. 4, pp. 248-255 (1980).
57. Glover, F., “Tabu Search, Part I,” ORSA Journal on Computing Vol. 1, No. 3, pp.190-206 (1989).
58. Glover, F., “Tabu Search- Part II,” ORSA Journal on Computing, Vol. 2, No. 1, pp. 4-32 (1990).
59. Glover, F. and Laguna, M., “Tabu Search, Kluwer Academic Publishers,” Massachusetts (1997).
60. Goldberg, D. E., “Genetic Algorithms in Search, Optimization, and Machine Learning,” Addison-Wesley, Reading MA (1989).
61. Golden, B. L. and Skiscim, C. C., “Using Stimulated Annealing to Solve Routing and Location Problems,” Naval Research Logistic Quarterly, Vol. 33, pp. 261-279 (1986).
62. Gu, J. and Huang, X., “Efficient Local Search with Search Space Smoothing: A Case Study of the Traveling Salesman Problem (TSP),” IEEE Transaction on Systems, Man and Cybernetics, Vol. 24, pp. 728-739 (1994).
63. Guisewite, G. M. and Pardalos, P. M., “A Polynomial Time Solvable Concave Network Flow Problems,” Networks, Vol. 23, pp. 143-147 (1993).
64. Hall, R. W., “Direct Versus Terminal Freight Routing on Network with Concave Costs,” GMR-4517, Transportation Research Dept., GM Research Laboratories (1983).
65. Hansen, P. and Mladenovic, N., “Variable Neighborhood Search,” Computers and Operations Research, Vol. 24, pp. 1097-1100 (2007).
66. Holland, J.H., “Adaptation in Natural and Artificial System,” University of Michigan Press (1975).
67. Hu, N.,”Tabu Search Method With Random Moves for Globally Optimal Design,” International Journal for Numerical Methods in Engineering, Vol. 35, pp. 1055–1070 (1992).
68. Jordan, W. C., “Scale Economies on Multi-Commodity Networks,” GMR-5579, Operating Systems Research Dept., GM Research Laboratories (1986).
69. Kennedy, J., Eberhart, R.C. and Shi, Y., “Swarm Intelligence,” Morgan Kaufmann division of Academin Press (2001).
70. Kennedy, J. and Spears, W., “Matching Algorithms to Problems: An Experimental Test of the Particle Swarm and Some Genetic Algorithms on the Multimodal Problem Generator,” In IEEE World Congress on Computational Intelligence, pp. 74–77 (1998).
71. Kershenbaum, A., “When Genetic Algorithms Work Best,” INFORMS Journal of Computing, Vol. 9, No. 3, pp.253-254 (1997).
72. Kirkpatrick, S., Gelatt, C. D. and Vecchi, M.P., “Optimization by Simulated Annealing,” Science, Vol. 220, pp. 671-680 (1983).
73. Kuhn, H. W. and Baumol, W. J., “An Approximate Algorithm for the Fixed-Charge Transportation Problem,” Naval Res. Logistics Quarterly, Vol. 9, pp. 1-16 (1962).
74. Kuntz, P. and Snyers, D., “Emergent Colonization and Graph Partitioning,” International Conference on Simulation of Adaptive Behaviour: From Animals to Animats, pp. 494-500 (1994).
75. Larsson, T., Migdalas, A. and Ronnqvist, M., “A Lagrangian Heuristic for the Capacitated Concave Minimum Cost Network Flow Problem,” European Journal of Operational Research, Vol. 78, pp. 116-129 (1994).
76. Liang, Y.C. and Chen, Y.C., “Redundancy Allocation of Series-Parallel Systems Using a Variable Neighborhood Search Algorithm,” Reliability Engineering and System Safety, pp. 323-331(2007).
77. Maniezzo, V. and A. Colorni, “The Ant system applied to the Quadratic Assignment Problem,” IEEE Trans. Knowledge and Data Engineering, Vol. 5, pp. 769-778 (1999).
78. Michael R. Garey and David S. Johnson., “Computers and Intractability: A Guide to the Theory of NP-Completeness,” W.H.Freeman and Company (1979).
79. N. Hu, “Tabu Search Method with Random Moves for Globally Optimal Design,” Int. J: Num. Meth. Engineering, vol. 35, pp. 1055-1070 (1992).
80. Nourie, F. J. and Guder, F., “A Restricted-Entry Method for a Transportation Problem with Piecewise-Linear Concave Cost,” Computer and Operations Research, Vol. 21, pp. 723-733 (1994).
81. Osman, I. H. and Kelly, J. P., “Meta-Heuristics: An Overview,” Meta-Heuristics: Theory and Applications, Kluwer Academic Publishers, Boston, London, Dordrecht, pp. 1-21 (1996).
82. Palmer, C. C. and Kershenbaum, A., “Representing Trees in Genetic Algorithms,” IEEE Conference on Evolutionary Computation, Vol. 1, pp. 379-384 (1994).
83. Powell, W. B, “A Review of Sensitivity Results for Linear Networks and a New Approximation to Reduce the Effects of Degeneracy,” Transportation Science, Vol. 26, No. 3, pp. 230-245 (1992).
84. Rech, P. and Barton, L. G., “A Non-Convex Transportation Algorithm,” Applications of Mathematical Programming Techniques, E. M. Beale, ed. (1970).
85. Reeves, C. R., “Improving the Efficiency of Tabu Search for Machine Sequencing Problems,” Journal of the Operation Research Society, Vol. 44, No. 4, pp. 375-382 (1994).
86. Reeves, C. R., “Genetic Algorithms for the Operations Researcher,” INFORMS J on Computing, Vol. 9, pp. 231-250 (1997).
87. Robuste, F., Daganzo, C. F. and Souleyrette, R., “Implementing Vehicle Routing Models,” Transportation Research, Vol. 24B, No. 4, pp. 263-286 (1990).
88. Salerno, J., Sinton, S. and Rahmar-Samii, Y., “Particle Swarm, Genetic Algorithm, and Their Hybrids: Optimization of a Profiled Corrugated Horn Antenna,” IEEE Antennas and Propagation Society, AP-S International Symposium, pp. 314-317 (1997).
89. Sheffi, M. J., Urban Transportation Networks:Equilibrium Analysis with Mathematical Programming Methods, Prentical-Hall (1984).
90. Shi, Y. and Eberhart, R. C. “A Modified Particle Swarm Optimizer,” Proceedings of the IEEE International Conference on Evolutionary Computation, pp. 69-73. IEEE Press, Piscataway, NJ (1998a).
91. Shi, Y. and Eberhart, R. C. “Parameter Selection in Particle Swarm Optimization,” Evolutionary Programming, Vol. 7, Proc. EP 98, pp. 591-600. Springer-Verlag, New York (1998b).
92. Suwan, R. and Sawased, T., “Link Capacity Assignment in Packet- Switched Networks: The Case of Piecewise Linear Concave Cost Function,” IEICE Trans. Commun., Vol. E82-B, No. 10 (1999).
93. Taguhi, T., Ida, K. and Gen, M., “A Genetic Algorithm for Optimal Flow Assignment in Computer Network,” Computers and Industrial Engineering, Vol. 35, pp. 535-538 (1998).
94. Thach, P. T., “A Decomposition Method Using A Pricing Mechanism for Min Concave Cost Flow Problems With a Hierarchical Structure,” Mathematical Programming, Vol. 53, pp. 339-359 (1992).
95. Yaged, B., “Minimum Cost Routing for Static Network Models,” Networks, Vol. 1, pp 139-172 (1971).
96. Yan, S. and Luo, S. C., “A Tabu Search-based Algorithm for Concave Cost Transportation Network Problems,” Journal of the Chinese Institute of Engineers, Vol. 21, pp. 327-335 (1998).
97. Yan, S. and Luo, S. C., “Probabilistic Local Search Algorithms for Concave Cost Transportation Network Problems,” European Journal of Operational Research, Vol. 117, pp. 511-521 (1999).
98. Yan, S., Juang, D. H., Chen, C. R. and Lai, W. S., “Global and Local Search Algorithms for Concave Cost Transshipment Problems,” Journal of Global Optimization, Vol. 33, No. 1, pp. 123 – 156 (2004).
99. Yan, S. and Young, H. F., “A Decision Support Framework for Multi-Fleet Routing and Multi-Stop Flight Scheduling,” Transportation Research, Vol. 30A, pp. 379-398 (1996).
100. Zangwill, W. I., “Minimum Concave Cost Flows in Certain Networks,” Management Science, Vol. 14, pp. 429-450 (1968).
101. Geem, Z. W., Tseng, C. L. and Park, Y., “Harmony Search for Generalized Orienteering Problem: Best Touring in China,” Springer-Verlag Berlin Heidelberg, pp. 741-750 (2005).
102. Geem, Z. W. and Kim, J. H., “A New Heuristic Optimization Algorithm: Harmony Search,” Simulation, pp. 60-68 (2001).
103. Geem, Z. W. and Lee, K. S., “A New Meta-Heuristic Algorithm For Continuous Engineering Optimization: Harmony Search Theory And Practice,” Comput. Methods Appl. Mech. Engrg., pp. 3902-3933 (2004).
104. Geem, Z. W., Lee, K. S., Lee, S. H. and Bae, K. W., “The Harmony Search Heuristic Algorithm For Discrete Structural Optimization,” Engineering Optimization, Vol. 37, No. 7, pp. 663-684 (2005).
指導教授 顏上堯(Shang-Yao Yan) 審核日期 2008-7-23
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