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农机作业路径优化的研究——基于旅行商问题新算法 被引量:3

The Optimization of the Path of Agricultural Operations——Based on a New Algorithm for Traveling Salesman Problem
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摘要 基于旅行商问题的特点与性质,设计了一种新的启发式算法。算法设计上首先从降维角度出发,先对图上各节点进行区域划分,然后在区域内部找到最小生成树,再将大于2的节点打断重新连接成除始点和终点外度均为2的树,最后相邻区域始点终点相连便可得到问题的解。通过两个实例论证说明,此种启发式算法可以有效地避免不同区域之间若干点的往返。基于此优点,将该算法应用到农机作业路径的优化中,为农机户的日常作业经营提供参考。 Based on the characteristics and properties of the traveling salesman problem,design a new heuristic algorithms.First,from algorithm design dimensionality reduction perspective,the first of the map for a regional breakdown of each node,and then inside the region to find the minimum spanning tree,and then interrupted by more than two nodes connected in addition to re-start and end points are the two outer degree of tree,and finally connected to the end adjacent areas can be the starting point solution of the problem.Demonstrated through two examples illustrate,this heuristic algorithm can effectively avoid a number of points between the different areas of the round trip.Based on this advantage,the algorithm is applied to the optimization of the path of agricultural operations,in order to run daily operations of agricultural households to provide a reference.
作者 杨巍 刘占良
出处 《农机化研究》 北大核心 2014年第6期54-57,共4页 Journal of Agricultural Mechanization Research
基金 国家谷子产业体系专项(2011-2015)
关键词 旅行商问题 启发式算法 区域 最小生成树 农机作业 路径优化 traveling salesman problem heuristic algorithm region minimum spanning tree agricultural operations path optimization
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  • 1苏丽杰,聂义勇.旅行商问题典型算法的综合性能[J].信息与控制,2003,32(z1):686-691. 被引量:6
  • 2屈稳太,丁伟.一种改进的蚁群算法及其在TSP中的应用[J].系统工程理论与实践,2006,26(5):93-98. 被引量:11
  • 3Kirkpatrick S, Gelatt C D, Vecchi M P. Optimization by simulated annealing[J]. Science, 1983, 220:671 680.
  • 4AnsariNandHouE 李军 边肇棋译.用于最优化的计算智能[M].北京:清华大学出版社,1999..
  • 5Cornuejols G, Fonlupt J and Naddef D. The traveling salesman problem on a graph and some related integer polyhedra[J].Mathematical Programming, 1985,33:1-27.
  • 6Siqueira P H,Steiner M T A,Scheer S.A New Approach to Solve the Traveling Salesman Problem[J].Neurocomputing,2007,70(4-6):1013-1021.
  • 7Liu Guangyuan,He Yi,Fang Yonghui,et al.A Novel Adaptive Search Strategy of Intensification and Diversification in Tabu Search[C]//Proc.of Conf.on Neural Networks and Signal Processing.Nanjing,China:[s.n.],2003.
  • 8Dorigo 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,1996,26(1):1-13.
  • 9Feo T A,Resende M G C.A Probabilistic Heuristic for a Computationally Difficult Set Covering Problem[J].Operations Research Letters,1989,8(4):67-71.
  • 10Depuy G W,Moraga R J,Whitehouse G E.Meta-RaPS:A Simple and Effective Approach for Solving the Traveling Salesman Problem[J].Transportation Research:Part E,2005,41:115-130.

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  • 1高尚,杨静宇,吴小俊,刘同明.圆排列问题的蚁群模拟退火算法[J].系统工程理论与实践,2004,24(8):102-106. 被引量:9
  • 2Applegate DL, Bixby RE, Chvatal V, et al. The Traveling Salesman Problem: A Computational Study. Princeton: Princeton University Press, 2011.
  • 3Kennedy JF, Eberhart RC. Particle swarm optimization. Procof IEEE International Conference on Neural Networks. Perth, Australia. 1995. 1942 -1948.
  • 4Yah ZP, Deng C, Zhou J J, Chi DN. A novel two-subpopulation particle swam optimization. Proc. of 10th Intelligent Control and Automation. Beijing, China. 2012. 4113-4117.
  • 5Muruganandham A, Wahida Banu Dr RSD. Adaptive fractal image compression using PSO. Procedia Computer Science, 2010, 2: 338-344.
  • 6Ding W. A new method for image noise removal using chaos PSO and nonlinear ICA. Procedia Engineering, 2011, 24: 111-115.
  • 7Yu ZF, Li JW, Liu K. Radar emitter recognition based on PSO-BP network. AASRI Procedia, 2012, 1: 213-219.
  • 8Hu Y, Wang XH, PSO-based energy-balanced double cluster-heads clustering routing for wireless sensor networks. Procedia Engineering, 2011, 15: 3073-3077.
  • 9Applegate DL,Bixby RE,Chvatal V,et al.The Traveling Salesman Problem: A Computational Study.Princeton:Princeton University Press,2011.
  • 10Holland JH.Adaptation in Natural and Artificial Systems.Ann Arbor: University of Michigan Press,1975.

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