Solving the Hamiltonian cycle problem via an artificial neural network

被引：4
|
作者：
Tambouratzis, T [1 ]
机构：
[1] NCSR Demokritos, Inst Nucl Tecnol Radiat Protect, Athens 15310, Greece
来源：
INFORMATION PROCESSING LETTERS | 2000年 / 75卷 / 06期
关键词：
combinatorial optimization; maximal constraint satisfaction; graph theory; Hamiltonian cycle; parallel algorithms; artificial neural networks; harmony theory;
D O I：
10.1016/S0020-0190(00)00116-2
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
An artificial neural network (ANN) is proposed for solving the Hamiltonian cycle problem of graph theory. The ANN automatically determines whether the proposed solution constitutes a Hamiltonian cycle. The ANN is also capable of uncovering all the Hamiltonian cycles of the given graph as well as of specifying the origin and direction of the Hamiltonian cycles produced. (C) 2000 Elsevier Science B.V. All rights reserved.
引用
收藏
页码：237 / 242
页数：6
相关论文
共 50 条
  • [31] Solving the control problem for electrochemical geartooth-profile modification using an artificial neural network
    Yi, J
    Zheng, J
    Yang, T
    Xia, D
    Hu, D
    INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2002, 19 (01): : 8 - 13
  • [32] Solving the Control Problem for Electrochemical Geartooth-Profile Modification Using an Artificial Neural Network
    J. Yi
    J. Zheng
    T. Yang
    D. Xia
    D. Hu
    The International Journal of Advanced Manufacturing Technology, 2002, 19 : 8 - 13
  • [33] Solving the forward kinematics problem in parallel manipulators using an iterative artificial neural network strategy
    Pratik J. Parikh
    Sarah S. Lam
    The International Journal of Advanced Manufacturing Technology, 2009, 40 : 595 - 606
  • [34] Hybrid Artificial Neural Network by Using Differential Search Algorithm for Solving Power Flow Problem
    Abaci, Kadir
    Yamacli, Volkan
    ADVANCES IN ELECTRICAL AND COMPUTER ENGINEERING, 2019, 19 (04) : 57 - 64
  • [35] Artificial neural network for solving flow shop optimization problem with sequence independent setup time
    Sadki, Hajar
    Allali, Karam
    Foundations of Computing and Decision Sciences, 49 (04): : 355 - 383
  • [36] Solving transcendental equation using artificial neural network
    Jeswal, S. K.
    Chakraverty, S.
    APPLIED SOFT COMPUTING, 2018, 73 : 562 - 571
  • [37] Solving the Job Sequencing and Tool Switching Problem as a Nonlinear Least Cost Hamiltonian Cycle Problem
    Ghiani, Gianpaolo
    Grieco, Antonio
    Guerriero, Emanuela
    NETWORKS, 2010, 55 (04) : 379 - 385
  • [38] Artificial Neural Network for Identification Of Heart Problem
    Azra'ai, Rohaida Ahmad
    bin Taib, Mohd Nasir
    Tahir, Nooritawati Md
    ICSPCS: 2ND INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING AND COMMUNICATION SYSTEMS, PROCEEDINGS, 2008, : 346 - 351
  • [39] Artificial neural network for the configuration problem in solids
    Ji, Hyunjun
    Jung, Yousung
    JOURNAL OF CHEMICAL PHYSICS, 2017, 146 (06):
  • [40] Solving Multi-Response Optimization Problem Using Artificial Neural Network and PCR-VIKOR
    Bashiri, Mahdi
    Geranmayeh, Amir Farshbaf
    Sherafati, Mahtab
    2012 INTERNATIONAL CONFERENCE ON QUALITY, RELIABILITY, RISK, MAINTENANCE, AND SAFETY ENGINEERING (ICQR2MSE), 2012, : 1033 - 1038
12345