TY - GEN
T1 - A columnar competitive model with simulated annealing for solving combinatorial optimization problems
AU - Eu, Jin Teoh
AU - Huajin, Tang
AU - Kay, Chen Tan
PY - 2006/7
Y1 - 2006/7
N2 - One of the major drawbacks of the Hopfield network is that when it is applied to certain polytopes of combinatorial problems, such as the traveling salesman problem (TSP), the obtained solutions are often invalid, requiring numerous trial-and-error setting of the network parameters thus resulting in low-computation efficiency. With this in mind, this article presents a columnar competitive model (CCM) which incorporates a winner-takes-all (WTA) learning rule for solving the TSP. Theoretical analysis for the convergence of the CCM shows that the competitive computational neural network guarantees the convergence of the network to valid states and avoids the tedious procedure of determining the penalty parameters. In addition, its intrinsic competitive learning mechanism enables a fast and effective evolving of the network. Simulation results illustrate that the competitive model offers more and better valid solutions as compared to the original Hopfield network.
AB - One of the major drawbacks of the Hopfield network is that when it is applied to certain polytopes of combinatorial problems, such as the traveling salesman problem (TSP), the obtained solutions are often invalid, requiring numerous trial-and-error setting of the network parameters thus resulting in low-computation efficiency. With this in mind, this article presents a columnar competitive model (CCM) which incorporates a winner-takes-all (WTA) learning rule for solving the TSP. Theoretical analysis for the convergence of the CCM shows that the competitive computational neural network guarantees the convergence of the network to valid states and avoids the tedious procedure of determining the penalty parameters. In addition, its intrinsic competitive learning mechanism enables a fast and effective evolving of the network. Simulation results illustrate that the competitive model offers more and better valid solutions as compared to the original Hopfield network.
KW - Combinatorial optimization
KW - Competitive learning
KW - Simulated annealing
KW - Traveling salesman problem
UR - https://www.scopus.com/pages/publications/40649109112
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-40649109112&origin=recordpage
U2 - 10.1109/ijcnn.2006.1716542
DO - 10.1109/ijcnn.2006.1716542
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 0-7803-9490-9
SP - 3254
EP - 3259
BT - The 2006 IEEE International Joint Conference on Neural Network Proceedings
PB - IEEE
T2 - 2006 International Joint Conference on Neural Networks (IJCNN '06)
Y2 - 16 July 2006 through 21 July 2006
ER -