跳到主要內容

臺灣博碩士論文加值系統

(3.235.140.84) 您好!臺灣時間:2022/08/13 05:01
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

: 
twitterline
研究生:蔡崇煒
研究生(外文):Chun-Wei Tsai
論文名稱:多重搜尋基因演算法:一個新的有效解決通訊網路及資料庫中複雜問題之方法
論文名稱(外文):MSGA: A Novel and Efficient Algorithm for Solving Complex Communication Network and Database Problem
指導教授:蔡正發蔡正發引用關係
指導教授(外文):Cheng-Fa Tsai
學位類別:碩士
校院名稱:國立屏東科技大學
系所名稱:資訊管理系
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2002
畢業學年度:90
語文別:中文
論文頁數:105
中文關鍵詞:遺傳基因演算法旅行銷售員問題群播繞送問題固定頻道分配問題
外文關鍵詞:Genetic algorithmsTraveling Salesman ProblemMulticast routingFixed channel assignmentData clustering
相關次數:
  • 被引用被引用:10
  • 點閱點閱:793
  • 評分評分:
  • 下載下載:198
  • 收藏至我的研究室書目清單書目收藏:0
  近年來,使用啟發式方法解決資訊科學領域中各種複雜問題已有非常多顯著的成果。這些方法包含模擬退火演算法 (SA)、 禁忌搜尋演算法 (TS)、螞蟻系統 (AS)、類神經網路 (NN)以及遺傳基因演算法 (GA)等等。在啟發式方法解決資訊科學領域問題中,GA是這些有效方法中較為著名的一種。其原因是因為GA的架構在複雜問題中搜尋最佳解的能力。但於它類數學問題時GA卻可能須重新設計其結構。由於其演化特性,將傳統GA應用於實際問題及工程問題時,其彈性仍略顯不足。上述問題同時也成為一個難以解決、相矛盾及多工之問題。因此,修改GA的結構、適應性函數、交配及突變的設定以及微調的問題,以改善GA之效能,是這類研究中較常見的方式。在本論文中,我們針對這些方式的效益評估比較,並提出一有效的方法,稱為多重搜尋基因演算法 (MSGA)。此一方法在經由實驗證明,其成效超越傳統GA、疫苗式(immune) GA及其它GA之方法。這些被驗証之問題包含旅行銷售員問題、群播繞送問題、固定頻道分配問題及資料分群問題等。經由實際電腦測試及驗証結果顯示,本論文所提之方法在這些問題中確實優於其它現存的GA方法。
  Recently, using the heuristic method to solve problems of computer science has many remarkable results. These are simulated annealing (SA), Tabu Search (TS), ant system (AS), neural network (NN), genetic algorithm (GA) and so forth. GA is known as one of the most efficient algorithm for various problems of computer science. The GA mechanism possesses the unique ability to search and optimize a solution for a complex system, where other mathematical oriented techniques may have failed to compile the necessary design specification. Due to its evolutionary characteristics, a standard GA may not be flexible enough for a practical application, and an engineering insight is always required whenever a GA is applied. This becomes more apparent where the problem to be tackled is complicated, conflicting and multi-tasking. Therefore, a means of modifying the GA structure, fitness function, operator settings of crossover and mutation, and fine-tuned problem, are sought in order to meet the design requirements. In this thesis we modify the GA structure to improve the performance significantly. We also present a deceptively simple in this thesis, yet empirically powerful metaheuristic, called multiple-searching genetic algorithm (MSGA), that has been found to more effective than traditional genetic algorithm, immune genetic algorithm, and other existing GA approaches for the TSP on benchmark test problems, multicast routing, fixed channel assignment and data clustering from the literature. According to our simulation results, our proposed algorithm is robust and outperforms the other existing GA’s in the previous mentioned problems such as TSP, multicast routing and data clustering problems.
目 錄
中文摘要……………………………………………………Ⅰ
英文摘要……………………………………………………Ⅲ
誌謝……………………………………………………Ⅴ
目錄 ……………………………………………………………………Ⅵ
圖目錄 ……………………………………………………………… Ⅸ
表目錄 ……………………………………………………………ⅩⅢ
第一章 緒論……………………………………………………………01
第二章 多重搜尋演算法 ………………………………………..05
第一節 傳統基因演算法……………………………………………05
一、Representation.……………………………………………......08
二、Evaluation (Fitness) function…………………………..…....08
三、Operator setting of crossover and mutation……………..08
四、Fine-tuning…………………………………………………….09
第二節 多重搜尋基因演算法 .………………………………….09
一、理論及概念 …………………………………………………...09
二、MSGA演算法…………………………………………………..12
三、MSGA的程序…………………………………………………..13
四、建立探險者染色體的方法 ………….………………….....15
第三節 MSGA與傳統GA的比較………………………………..18
第四節 結論 ………………………………………………………….19
第三章 應用MSGA於旅行銷售員問題 ………………….20
第一節 問題定義…………………………………………………….20
第二節 相關研究…………………………………………………….21
第三節 實作程序…………………………………………………….22
一、利用最近鄰居及候選人機制建立探險者染色體………..23
二、使用較佳的方式避免落入區域解 …………………………24
第四節 實驗結果……………………………………………………25
第五節 結論…………………………………………………………….30
第四章 利用MSGA求解群播繞送問題……………………32
第一節 問題定義……………………………………………………...32
第二節 相關研究………………………………………………….34
第三節 實作程序…………………………………………………….35
第四節 實驗結果…………………………………………………….37
第五節 結論……………………………………………………………42
第五章 以MSGA解固定頻道分配之問題…………….43
第一節 問題定義…………………………………………………….43
第二節 相關研究…………………………………………………….47
第三節 實作程序………………………………………….…………48
第四節 實驗結果……………………………………………….…....63
第五節 結論………………………………………………………….70
第六章 使用MSGA求解資料分群問題…………………72
第一節 問題定義………………………………….…………………72
第二節 相關研究……………………………………………………75
第三節 實作程序…………………………………………………….78
第四節 實驗結果……………………………………………………80
第五節 結論……………………………………………………………84
第七章 結論及未來研究方向………………………………….85
參考文獻…….……………………………………………………….…89
作者簡介 ….…………………………………..…………………….…103
圖目錄
圖2-1:Selection…………………………………………………………..06
圖2-2:Crossover…………………………..……………………………..06
圖2-3:Mutation…………………………………………………….……06
圖2-4:Process of the Genetic Algorithm……………………………07
圖2-5:在求解的最後階段,所有的解會成為相似的一群………10
圖2-6:解空間及最佳解的關係………………………………………11
圖2-7A:TGA收斂程序一……………………………………………...11
圖2-7B:TGA收斂程序二……………………………………………...11
圖2-8A:MSGA收斂程序一………………………………………….12
圖2-8B:MSGA收斂程序二………………………………………….12
圖2-9:Process of the Genetic Algorithm………………………..…13
圖2-10:MSGA計算程序步驟1及步驟2…………………………..14
圖2-11:MSGA計算程序步驟3…………………….………………..15
圖2-12:候選人機制……………………………………………..……….16
圖2-13:路徑的建立……………………………………………………..17
圖2-14:MSGA建立探險者的程序………………………………….17
圖2-15:傳統GA染色體進行演化的程序…………………………18
圖2-16:MSGA染色體進行演化的程序…………………………….18
圖3-1:染色體狀態………………………………………………………24
圖3-2:發生衝突…………………………………………………………25
圖3-3:MSGA1.0發生衝突的解決方法…………………………….25
圖3-4:MSGA1.1發生衝突的解決方法…………………………….25
圖3-5:Benchmark-ei151………….……………………………......27
圖3-6:Benchmark-ei176………..…………………………………….28
圖3-7:Benchmark-kroa100…..……………………………………….28
圖3-8:Benchmark-d198……….………………………………………29
圖3-9:Benchmark-ts225……………………………………………….29
圖3-10:Benchmark-pcb442……………………………………….…30
圖4-1:Ren-Hung Hwang 的表示法……..………………………….36
圖4-2:Qingfu Zhang的表示法……………………………………..36
圖4-3:Yee Leung的表示法…………………………………………..36
圖4-4:16 nodes (|D|=8)………………….……………………………38
圖4-5:20 nodes (|D|=10)…………………………………………….38
圖4-6:32 nodes (|D|=16) ……………………….…………………….38
圖4-7:50 nodes (|D|=25) ……………………………………………….38
圖4-8:64 nodes (|D|=32) ….………………………………………….38
圖4-9:20點問題之結果………………………………………………...40
圖4-10:32點問題之結果…….……………………………………...40
圖4-11:50點問題之結果…………………………………………….41
圖4-12:64點問題之結果…………………………………………….41
圖4-13:50點問題之結果…………………………………………….41
圖4-14:64點問題之結果……………………………………………41
圖5-1:matrix C…………………………………………………………44
圖5-2:demand d……………………………………………………….44
圖5-3:the optimum for this FCA problem……………………….45
圖5-4:Channel配置前之狀態…………………………………….50
圖5-5:Channel配置前……………………………………………....50
圖5-6:Initialize 程序1-1…………………………………………….51
圖5-7:Initialize 程序1-2…………………………………………….51
圖5-8:Initialize 程序2-1…………………………………………….52
圖5-9:Initialize 程序2-2…………………………………………….52
圖5-10:Initialize 程序3-1 ……………………………………………53
圖5-11:Initialize 程序3-2 ……………………………………………53
圖5-12:Initialize 程序4-1……………………………………………54
圖5-13:Initialize 程序4-2……………………………………………54
圖5-14:Initialize 程序5………………………………………………54
圖5-15:Uniform crossover…………………………………………….56
圖5-16:One point crossover………………………………………….56
圖5-17:Two point crossover………………………………………….57
圖5-18:New local search example………………………………….60
圖5-19:New local search algorithm………………………………61
圖5-20:MSGA algorithm……………………….……………………62
圖5-21:MSGA process………………….……………………………..63
圖5-22:Specifications of Simulated Problems……………………..63
圖5-23:Compatibility matrices and demand vectors in simulated
problems……………………………………………………….65
圖5-24:Compare MSGA (Local Search) with GFA
in Problem 8………………….……………………………69圖6-1:Hierarchical的分群方法………………………………….…..76
圖6-2:MS-GKA algorithm…………………………………………...79
圖6-3:579 node-from Mu-Chun Su…….………………………...80
圖6-4:演化過程(579點)………………………………………………81
圖6-5:演化過程(1000點)……………………………………………..82
圖6-6:演化過程(2000點)……………………………………………..83
表目錄
表3-1:基本參數設定…………………………………………………26
表3-2:演化代數設定…………………………………………………26
表 3-3:TGA、IGA、MSGA1.0、MSGA1.1及
MSGA1.1+2-opt的比較…........................................……….27
表4-1:參數設定1………………………………………………………39
表4-2:參數設定2………………………………………………………39
表4-3:HGA、MHGA、LGA、MLGA、OGA及MOGA的比較…39
表4-4:HGA、MHGA(MSGA)的比較………………………………40
表4-5:LGA、MLGA(MSGA)的比較…………………………………41
表4-6:OGA、MOGA(MSGA)的比較………………………………42
表5-1:參數設定…………………………………………………………66
表5-2:TGA with the proposed initialization method…………….66
表5-3:MSGA with the proposed initialization method……………67
表5-4:Compare MSGA with TGA、KGA and NN……………….68
表5-5:參數設定…………………………………………………………68
表5-6:比較Neural Network[6]、TGA[17]、GFA[2]、MSGA及
MSGA+Local Search…………………………………………69
表6-1:實驗結果-within cluster (579點)…………………………….81
表6-2:實驗結果-between cluster (579點) ………………..………81
表6-3:實驗結果-within cluster (1000點)…………………………82
表6-4:實驗結果-between cluster (1000點) ……………………….82
表6-5:實驗結果-within cluster (2000點) …………….………….83
表6-6:實驗結果-between cluster (2000點) ……………………83
[1] J.H.Holland, Adaption in Natural and Artificial System, Boston, MA: MIT Press, 1992.
[2] M.Mitchell, An Introduction to Genetic Algorithms. Cambridge, MA: MIT Press, 1996.
[3] Z.Michalewicz, Genetic Algorithms + Data Structures = Evolution Programs, 2nd ed. New York: Springer-Verlag, 1994.
[4] Hopfield, J. and Tank, D., “Neural computations of decisions inoptimizaton problems,” Biological Cybernetics, vol.51, pp. 141-152, 1958.
[5] D.E. Van den Bout and T.K. Miller, “A traveling salesman object function that work,” ICNN-88, vol. 2, pp.299-303, 1988.
[6] D.E. Van den Bout and T.K. Miller, “Graph partitioning using annealed neural network,” IEEE Transactions on Neural Netowrks, vol. 1, no. 2, pp.192-203, 1990.
[7] F.Glover, “Heuristics for integer programming using surrogate constraints,” Decision Science, vol. 8, pp.156-166, 1977.
[8] F.Glover, “Tabu search-part I,” ORSA Journal of computing, vol. 1, no. 3, pp. 190-206, 1989.
[9] F.Glover, “Future paths for integer programming and links to artificial intelligence,” Computers and Operations Research, vol. 13, no. 5, pp. 533-549, 1986.
[10] Marco Dorigo, Vittorio Maniezzo and Alberto Colorni, “Ant System: Optimization by a Colony of Copperating Agents,” IEEE Transactions on System, Man and Cybernetics-Part B, vol. 26, no. 1, February 1996.
[11] Marco Dorigo and Luca Maria Gambardella, “Ant Colony System: A Cooperative Learning Approach to the Traveling Salesman Problem,” IEEE Transactions on Evolutionary Computation, vol. 1, no. 1, April 1997.
[12] Marco Dorigo, “Learning by probabilistic Boolean networks,” IEEE Neural Networks, vol. 2, pp. 887-891, 1994.
[13] Marco Dorigo , Vittorio Maniezzo and Alberto Colorni, “The Ant System: An Autocatalytic Optimizing Process,” Technical Report no. 91-016, Politecnico di Milano, Italy 1991.
[14] Marco Dorigo, Gianni Di Caro and Luca M. Gambardella, “Ant Algorithm for Discrete Optimization,” Artifical Life, vol. 5, no. 2, pp. 137-172, 1999.
[15] S.Lin and B.W. Kernighan, “An effective heuristic algorithm for traveling salesman problem,” Oper. Res., vol. 21, pp. 498-516, 1973.
[16] K.Krishna and M. Narasimha Murty, “Genetic K-Means Algorithm,” IEEE Transactions on System, Man and Cybernetics-Part B:Cybernetics, vol. 29, no. 3,pp. 433-439, 1998.
[17] Lei Wang and Licheng Jiao, “A Novel Genetic Algorithm based on Immunity,” IEEE Circuits and Systems, vol. 5, pp. 385-388, 2000.
[18] Lei Wang and Licheng Jiao, “A Novel Genetic Algorithm based on Immunity,” IEEE Transactions on Systems, Man and Cybernetics: Part A, vol. 30, no. 5, pp. 552-561, 2000.
[19] Chun-Wei Tsai, Cheng-Fa Tsai, and Chi-Ping Chen, “A Novel Multiple-Searching Genetic Algorithm for Multimedia Multicast Routing,” 2002 IEEE Congress on Evolutionary Computation (CEC 2002), Honolulu, Hawaii, USA (Accepted, paper no: 7065).
[20] Cheng-Fa Tsai, Chun-Wei Tsai, Chi-Ping Chen, and Feng-Cheng Lin, “A Multiple-Searching Approach to Genetic Algorithms for Solving Traveling Salesman Problem,” Sixth International Conference on Computer Science and Informatics (CS&I 2002), pp. 362-366, Durham, NC, USA, March, 2002.
[21] Cheng-Fa Tsai, Chun-Wei Tsai, Chi-Ping Chen, “A New Method for Multimedia Multicast Routing in a Large Scale Network,” 2002 IEEE International Parallel and Distributed Processing Symposium (IPDPS 2002), In CD-ROM paper #246, Fort Lauderdale, Florida, USA.
[22] Cheng-Fa Tsai and Chun-Wei Tsai, and Han-Chan Wu,“A Novel Multimedia Multicast Routing Approach for the Internet,” 2001 IEEE International Conference on Multimedia and EXPO, Tokyo, Japan, pp. 589-592, Aug. 2001.
[23] Andrew Tuson and Peter Ross, Adapting Operator Settings in Genetic Algorithms, Evolutionary Computation, MIT Press, vol. 6, no. 2, Summer 25, 1998.
[24] Mark H.Noschang WebPage, http://www.ececs.uc.edu/~mnoschan/sale.html
[25] E.L.Lawer, J.K. Lenstra, A.H.R.Kan, and D.B.Shmoys,The Traveling Salesman Problem, New York:Wiley, 1985.
[26] M.M. Flood, ”The Traveling Salesman Problem,” Operation Research, vol. 4, pp. 61-78, 1955.
[27] M.Gen and R.cheng,Genetic Algorithms and Engineering Design, New York:Wiley, 1997.
[28] W.P. Poon, J.N.Carter, “Genetic Algorithm Crossover Operators for Ordering Applications,” Computers and Operations Research, vol. 22, no. 1, pp. 135-147, 1995.
[29] Shinn-Ying Ho and Jian-Hung Chen,”A GA-based Systematic Reasoning Approach for Solving Traveling Salesman Problems Using an Orthogonal Arry Crossover,” IEEE High Performance Computing, vol. 2, pp. 659-663, 2000.
[30] TAKENAKA Yoichi and FUNABIKI Nobuo, “An Improved Genetic Algorithm Using the Convex Hull for Traveling Salesman Problem,” Systems, Man andCybernetics, vol. 3 , pp. 2279-2284, 1998.
[31] W.N.Martin, A.L. Barker and J.P.Cohoon, ”Problem Perturbation: Implications on the Fitness Landscape,” IEEE Evolutionary Computation, vol. 1, pp. 744-751, 1999.
[32] Hiroyuki Shirai, Atushi Ishigame, Shunji Kawamoto and Tsuneo Taniguchi ,”A Solution of Combinatorial Optimization Problem by Uniting Genetic Algorithms with Hopfield’s Model,” IEEE Computational Intelligence, vol. 7, pp. 4704-4709, 1994.
[33] Satoshi ENDOH, Naruaki TOMA and YAMADA,”Immune algorithm for n-TSP,” IEEE Systems,Man and Cybernetics, vol.4, pp. 3844-3849, 1998
[34] http://wayne.cs.nthu.edu.tw/~roland/nn/index2c.html
[35] http://www.ing.unlp.edu.ar/cetad/mos/TSPBIB_home.html
[36] http://www.iwr,uni-heidelberg.de/iwr/compot/soft/
TSPLIB95/TSPLIB.html
[37] http://www.well.com/~xanthian/link_pages/Programming/
Paradigms/ComputationalComplexity.html
[38] http://www.pcug.org.au/~dakin/tsp.htm
[39] H.Eriksson, “Mbone: The multicast backbone,” Communications ACM, vol. 37, no. 8, pp. 54-60, Aug. 1994.
[40] J.Moy, “Multicast routing extensions for OSPF,” Communications ACM, vol.37, no.8, pp.61-66, Aug. 1994.
[41] Winter, P. , “Steiner problem in networks: a survey,” IEEE Network, vol. 17, no. 2 , pp. 129-167, 1987.
[42] F.K. Hwang, D. S. Richards, and P. Winter, The Steiner Tree Problem, North-Holland, 1992.
[43] X.Jia, “Group Multicast routing algorithm by using minimum Steiner trees,” Computer Communications 20, pp. 750-758, 1997.
[44] K.Bharath-Kumar and J.M. Jaffe, “Routing to multiple destinations in computer networks,” IEEE Trans. Communication, vol. 31, no. 3 , pp. 343-351, Mar. 1983.
[45] S.L.Hakimi, “Steiner’s problem in graphs and its implications,” Networks, vol. 1, no. 1, pp. 113-133, 1971.
[46] R.M. Karp, “Reducibility among combinatorial problems,” Complexity of Computer Communications, R.E. Miller and J.W. Thatcher, Eds., Plenum, New York, pp. 85-103, 1972.
[47] M.R. Garey, D.S. Johnson, Computers and Intractability- a Guide to the Theory of NP-completeness, Freeman, New York, June 1988.
[48] THALER, D.G. and CHINYA, V.R., “Distributed center-location algorithms,” IEEE,J. Sel, Areas Communications, vol. 15, no. 3, 1997.
[49] T.Ballardie, P.Francis, and J. Crowcroft, “Core Based Tree(CBT), An Architecture for Scalable Inter-Domain Routing,” ACM-SIGCOMM’93 Conference, September 1993.
[50] T.Ballardie, “Core based tree(CBT) multicast:Architectural overview and specification,” Internet Draft RFC, July, 1994.
[51] BALLARDIE, A.J., A new approach to multicast communication in a datagram internetwork, PHD dissertation, UCL, 1995.
[52] Erol Gelenbe, Anoop Ghanwani, and Vijay Stinivasan, “Improved Neural Heuristics for Multicast Routing,” IEEE Journal on Selected Areas in Communications, vol. 15, no. 2, pp. 147-155 February 1997.
[53] Tom Billhartz, J.bib Cain, Ellen Farrey-Goudreau, Doug Fieg, and Stephen Gordon Batsell, “Performance and Resource Cost Comparisons for the CBT and PIM Multicast Routing Protocols,” IEEE Journal on Selected Areas in Communications, vol. 15, no.3, pp. 304-315, April 1997.
[54] David G. Thaler and Chinya V. Ravishankar, “Distributed Center-Location Algorithms,” IEEE Journal on Selected Areas in Communications, vol. 15, no. 3, pp. 291-303, April 1997.
[55] Yee Leung, Guo Li, and Zong-Ben Xu, “A Genetic Algorithm for the Multiple Destination Routing Problem,” IEEE Transactions on Evolutionary Computation, vol. 2, no. 4, November 1998.
[56] Qingfu Zhang and Yiu-Wing Leung, “An Orthogonal Genetic Algorithm for Multimedia Multicast Routing,” IEEE Transactions on Evolutionary Computation, vol. 3, no. 1, pp. 53-62, April 1999.
[57] J.Reeve, P.Mars and T.Hodgkinson, “Learning Algorithms for Multicast routing,” IEE Proc.-Communication,vol.146, no. 2, pp. 89-94, April 1999.
[58] Ren-Hung Hwang, Wei-Yuan Do and Shyi-Chang Yang, “Multicast Routing Based on Genetic Algorithms,” Journal of Information Science and Engineering 16, pp. 885-901, 2000.
[59] V.J. Rayward-Smith and A. Clare, “On finding Steiner vertices,” Networks, vol. 16, pp. 283-294, 1986.
[60] L.Kou, G. Markowsky, and L. Berman, “A fast algorithm for Steiner tree”, Acta Informatica, vol. 15, pp. 141-145, 1981.
[61] A.Kapsalis, V. J. Rayward-Smith, and G. D. Smith, “Solving the graphical Steiner tree problem using genetic algorithm,” J. Opl. Res. Soc., vol. 44, pp. 397-406, 1993.
[62] Smith, K.A., “A genetic algorithm for the channel assignment problem,” IEEE Global Telecommunications Conference, vol. 4 , pp. 2013 -2018, 1998.
[63] P. Raymond, “Performance analysis of cellular networks,” IEEE Trans. Commun., vol. 39, no. 12, pp. 1787-1793, 1991.
[64] K. N. Sivarajan, R. J. McEliece, and J. W. Ketchun, “Channel assignment in cellular radio,” IEEE Vehicular Technology Conf., pp. 846-850, May 1989.
[65] A. Gamst and W. Rave, “On frequency assignment in mobile automatic telephone systems,” IEEE GLOBECOM ’82, pp. 309-315, 1982.
[66] M. Sengoku, K. Nakano, K. Shinoda, Y. Yamaguchi, and T. Abe, “Cellular mobile communication systems and channel assignment using neural networks,” in Proc. IEEE 33rd Midwest Symp. Circuits and Syst., pp. 411-414, Aug. 1990.
[67] D. Kunz, “Channel assignment for cellular radio using neural network,” IEEE Trans. Veh. Technol., vol. 40, no 1, pp. 181-193, 1991.
[68] N. Funabiki and Y. Takefuji, “A neural network paralled algorithm for channel assignment problems in cellular radio networks,” IEEE Trans. Veh. Technol., vol. 41, no. 4, pp. 430-437, 1992.
[69] G. D. Lochite, “Frequency channel assignment using artificial neural networks,” IEEE Int. Conf. Antennas and Propagation, vol. 2, pp. 948-951, 1993.
[70] M. Duque-Anton, D. Kunz and B. Ruber, “Channel assignment for cellular radio using simulated annealing,” IEEE Trans. Veh. Technol., vol. 42, no. 1, pp. 14-21, 1993.
[71] R. Mathar and J. Mattfeldt, “Channel assignment in cellular radio networks,” IEEE Trans. Veh. Technol., vol. 42, no. 4, pp. 647-656, 1993.
[72] K. Smith and M. Palaniswami, “Static and Dynamic Channel Assignment using Neural Networks,” IEEE Journal on Selected Areas in Communications, vol. 15, no. 2, pp. 238-249, 1997.
[73] W. K. Lai and G. G. Coghill, “Channel assignment through evolutionary optimization,” IEEE Trans. Veh. Technol., vol. 45, no. 1, pp. 91-96, 1996
[74] C. Y. Ngo and V. O. K. Li, “Fixed Channel Assignment in Cellular Radio Networks Using a Modified Genetic Alogorithm,” IEEE Transactions on Vehicular Technology, vol. 47, no 1, February 1998.
[75] K. A. Smith, “A Genetic Algorithm for the Channel Assignment Problem,” Global Telecommunications Conference, vol. 4, pp. 2013 —2018, 1998.
[76] J.S. KIM, S.PARK, P.DOWD and N.NASRABADI, “Channel Assignment in Cellular Radio Using Genetic Algorithms,” Wireless Personal Communications, vol. 3, pp. 273-286, 1996.
[77] Juha Vesanto and Esa Alhoniemi, “Clustering of the Self-Organizing Map,” IEEE Transactions on Neural Netowrks, vol. 11, no. 3, pp.586-600, May 2000.
[78] A.K. Jain and R.C. Dubes, “Algorithms for Clustering Data”, N.J.:Prentice Hall, 1988.
[79] Hichem Frigui and Raghu Krishnapuram, “A Robust Competitive Clustering Algorithm With Applications in Computer Vision,” IEEE Transactions of Pattern Analysis and Machine Intelligence, vol. 21, no. 5, pp. 450-465, May 1999.
[80] J.-H. Wang and J.-D. Rau, “VQ-Agglomeration: a novel approach to clustering,” IEE Proc.-Vis., Image Signal Process., vol. 148, no. 1, pp. 36-44, February 2001.
[81]Sudipto Guha , Rajeev Rastogi and Kyuseok Shim, “CURE: An Efficient Clustering Algorithm For Large Database,” Information Systems(Elsevier Science), vol. 26, no. 1, pp. 35-58, 2001.
[82]Randall S. Sexton and Robert E. Dorsey, “Reliable classification using neural networks: a genetic algorithm and backpropagation comparision,” Decision Support System, vol . 30, pp. 11-22, 2000.
[83]Kimmo Uutela, Matti Hämäläinen and Riitta Salmelin, “Global Optimization in the Localization of Neuromagnetic Sources,” IEEE Transactions on Biomedical Engineering, vol. 45, no. 6, pp. 716-723, 1998.
[84]Hiroshi Ishikawa, Manabu Ohta and Koki Kato, “Document Warehousing: A Document-Intensive Application of A Multimedia Database,” Eleventh International on Data Engineering, pp. 25 -31, 2001.
[85] L. Lucchese and S.K. Mitra, “Unsupervised segmentation of color images based on k-means clustering in the chromaticity plane Lucchese,” IEEE Content-Based Access of Image and Video Libraries (CBAIVL ''99), pp.74 -78, 1999.
[86] T.Kohonen, “Self-organized formation of topologically correct feature maps,” Biol.Cybern., vol.43, pp.59-69, 1982.
[87] Kohonen, T., “The self-organizing map,” Proc. IEEE, vol. 78, no. 9, pp. 1461-1480, 1990.
[88] K.Obu-Cann, K.Iwamoto,H. Tokutaka and K. Fujimura, “Clustering by SOM (self-organising maps), MST (minimal spanning tree) and MCP (modified counter-propagation),” IEEE ICONIP ''99. 6th International Conference on Neural Information Processing, pp. 986 -991, vol. 3, 1999.
[89] Philipp Tomsich, Andreas Rauber and Dieter Merkl, “Optimizing the parSOM neural network implementation for data mining with distributed memory systems and cluster computing,” IEEE 11th International Database and Expert Systems Applications , pp. 661-665, 2000.
[90] Masahiro Endo, Masahiro Ueno, Takaya Tanabe and Manabu Yamamoto, M., “Clustering method using self-organizing map,” IEEE Neural Networks for Signal Processing X, pp. 261-270, vol. 1, 2000.
[91] Hiroshi DOUZONO, Shigeomi HARA and Yoshio NOGUCHI, “A clustering method of chromosome fluorescence profiles using modified self organizing map controlled by simulated annealing,” IEEE International Joint Conference on Neural Networks(IJCNN 2000), vol. 4, pp. 103-106, 2000.
[92] Mu-Chun Su and Hsiao-Te Chang, “Fast self-organizing feature map algorithm,” IEEE Transactions on Neural Networks, vol. 11, no. 3, pp. 721-733, May 2000.
[93] Mu-Chun Su and Hsiao-Te Chang, “Machine Learning: Neural Network, Fuzzy System and Genetic Algorithms,” Taiwan:OpenTech, 1999.
[94] S.P. Lloyd, “Least Squares Quantization in pcm,” Transaction on Inform. Theory, vol. IT-28, no. 2, pp. 129-137, 1982.
[95] Chinrungrueng C. and Qequin C.H., “Optimal adaptive k-means algorithm with dynamic adjustment of learning rate,” IEEE Transaction on Neural Networks, vol. 6, no. 1, pp. 157-169, Jan 1995.
[96] J.Bezdek and R. Hathaway, “Numerical convergence and interpretation of the fuzzy C-Shells clustering algorithm,” IEEE Transactions on Neural Networks, vol. 3, pp. 787-793, Sept. 1992.
[97] R.Krishnapuram, O. Narsraoui and H. Frigui, “The fuzzy C spherical Shells Algorithm:A new approach,” IEEE Trans. Neural Networks, vol. 3, pp. 787-793, Setp. 1992.
[98] Il Hong Suh, Jae-Hyun Kim and Frank Chung-Hoon Rhee, “Convex-Set-Based Fuzzy Clustering,” IEEE Transactions on Fuzzy System, vol. 7, no. 3, pp. 271-285, June 1999.
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top