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蚁群算法求解TSP综述 被引量:3

Survey on Ant Colony Algorithm for the Traveling Salesman Problem
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摘要 蚁群算法是一种群智能算法,可用于求解图模型最优化路径的计算问题.它于1992年由Dorigo M.提出,借鉴蚂蚁在蚁群与食物之间寻找最短路径.本文集中讨论了几种典型的求解旅行商问题的蚁群算法扩展,讨论其相应的优缺点,并对其学术与工业的应用领域与合理发展进行了总结与展望. Ant colony algorithm( ACA) is a swarm intelligence- based method for solving computational problems that can be reduced to finding good paths through graphs. It was initially proposed by M. Dorigo in 1992,inspired by the behavior of ants seeking a shortest paths between the colony and a source of food. The paper concentrates on the discussions of the typical ACA extension for solving the traveling salesman problem( TSP) and their respective advantages and disadvantages,and finally summarize and expect their academic and industrial applied fields and reasonable developments.
出处 《南京师范大学学报(工程技术版)》 CAS 2014年第4期39-44,共6页 Journal of Nanjing Normal University(Engineering and Technology Edition)
基金 国家自然科学基金(610011024) 南京师范大学高层次人才科研启动基金项目(2013119XGQ0061)
关键词 蚁群算法 蚂蚁系统 蚁群系统 最大最小蚂蚁系统 旅行商问题 ant colony algorith ant system ant colony system max-min ant system TSP
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参考文献33

  • 1许殿,史小卫,程睿.回归蚁群算法[J].西安电子科技大学学报,2005,32(6):944-947. 被引量:4
  • 2Dorigo M, Birattari M, Stutzle T. Ant colony optimization [ J ]. IEEE on Computational Intelligence Magazine, 2006,1 ( 4 ) : 28 -39.
  • 3Dorigo M, Maniezzo V, Colorni A. The ant system : optimization by a colony of cooperating agents [ J ]. IEEE Transactions on Systems, Man, and Cybernetics-Part B : Cybernetics, 1996,26 ( 1 ) :29-41.
  • 4Dorigo M. Optimization, Learning and Natural Algorithms [ D ]. Politecnico di Milano, Italy, 1992.
  • 5张煜东,吴乐南,韦耿.智能算法求解TSP问题的比较[J].计算机工程与应用,2009,45(11):11-15. 被引量:8
  • 6Zhang Y, Agarwal P, Bhatnagar V, et al. Swarm intelligence and its applications [ J]. The Scientific World Journal,2013 (2013) :528 069.
  • 7Hinchey M G, Sterritt R, Rouff C. Swarms and swarm intelligence [ J ]. Computer, 2007,40 ( 4 ) : 111 - 113.
  • 8De Castro L N. Fundamentals of natural computing:an overview[ J ]. Physics of Life Reviews ,2007,4( 1 ) :1-36.
  • 9Marinakis Y, Marinaki M, Doumpos M, et al. Ant colony and particle swarm optimization for financial classification problems [J].Expert Systems with Applications ,2009,36 (7) : 10 604-10 611.
  • 10Blum C, Valles M Y, Blesa M J. An ant colony optimization algorithm for DNA sequencing by hybridization [ J ]. Computers & Operations Research ,2008,35 ( 11 ) :3 620-3 635.

二级参考文献76

  • 1段海滨,王道波,于秀芬,朱家强.基于云模型理论的蚁群算法改进研究[J].哈尔滨工业大学学报,2005,37(1):115-119. 被引量:44
  • 2Haibin Duan,Daobo Wang,Xiufen Yu.Research on the Optimum Configuration Strategy for the Adjustable Parameters in Ant Colony Algorithm[J].通讯和计算机(中英文版),2005,2(9):32-35. 被引量:16
  • 3Guangwei Zhou,Albert Gan,L. David Shen.Optimization of Adaptive Transit Signal Priority Using Parallel Genetic Algorithm[J].Tsinghua Science and Technology,2007,12(2):131-140. 被引量:15
  • 4KASTNER R. Synthesis techniques and optimizations for reconfigurable systems[D]. Los Angeles: University of California, 2002.
  • 5ERNST R, HENKEL J, BENNER T. Hardware-software cosynthesis for microcontrollers[J]. IEEE Design & Test of Computers, 1993, 10(4): 64-75.
  • 6SAHA D, MITRA R S, BASU A. Hardware software partitioning using genetic algorithm[C]. Proc. of the 10th Int'l Conf. on VLSI Design. Hyderabad: IEEE Computer Society Press, 1997: 155-160.
  • 7KOUDIL M, BENATCHBA K, TARABET A, et al. Using artificial bees to solve partitioning and scheduling problems in codesign [J]. Applied Mathematics and Computation, 2007, 186(2): 1710-1722.
  • 8GAJSKI D D, VAHID F, NARAYAN S, et al. SpecSyn: An environment supporting the specify-explore-refine paradigm for hardware/software system design [J]. Readings in Hardware/Software Co-Design, 2002, 108- 124.
  • 9VAHID F, STITT G. Hardware/software partitioning [J]. Reconfigurable Computing, 2008: 539-560.
  • 10KALVADE A, LEE E A. The extended partitioning problem: hardware/software mapping, scheduling, and implementation-bin selection [J]. Design Automation of Embedded Systems, 1997, 2(1): 125-163.

共引文献62

同被引文献34

  • 1高鹰,谢胜利.混沌粒子群优化算法[J].计算机科学,2004,31(8):13-15. 被引量:103
  • 2焦李成,杜海峰,刘芳,等.免疫优化计算,学习与识别[M].北京;科学出版社,2007:93-104,133-143.
  • 3Garey M R, Graham R L, Johnson D S. Some NP-complete geomet ric problems[C]//Proc 8th Annu ACM Syrup: Theory of Computing Washington, America, 1976: 10-22.
  • 4Michalewicz Z, Fogel D B. How to solve it: ModernHeuristick[M]. BerlinHeidelberg: Springer, 2000: 58-78.
  • 5Holland J H. Adaptation in nature and aritificial systems[M]. Massachusetts, USA: MIT Press, 1992: 12-72.
  • 6杨奇文,姜金平,张国红.速度优化的遗传算法[J].软件学报,2001,1(2):270-275.
  • 7Goldberg D E. Genteic algorithms in search optimization and machine learning[M]. Massachusetts, USA: Addison-Wesley, 1989: 38-95.
  • 8Stutzle T, Hoos H H. Max-min ant system[J]. Future Generation Computer Systems, 2000, 16(8): 889-914.
  • 9段海滨.蚁群算法原理及其应用[J].北京:科学出版社,2006:45-96.
  • 10TSPLIB[EB/OL]. [2007-07-23]. http//www.iwruni-hei-delbergde/ groups/comopt/software/TSPLIB95/.

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