A hybrid neural approach to combinatorial optimization
Computers and Operations Research - Special issue: artificial intelligence, evolutionary programming and operations research
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Connectionist Models of Neurons, Learning Processes and Artificial Intelligence-Part I
Neural Networks for Combinatorial Optimization: a Review of More Than a Decade of Research
INFORMS Journal on Computing
Manufacturing cell formation using a new self-organizing neural network
Computers and Industrial Engineering - 26th International conference on computers and industrial engineering
Neural techniques for combinatorial optimization with applications
IEEE Transactions on Neural Networks
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The self-organising neural network with weight normalisation (SONN-WN) for solving combinatorial optimisation problems (COPs) is investigated in terms of its performance and dynamical characteristics. A simplified computational model of the weight normalisation process is constructed, which reveals symmetry-breaking bifurcations in a typical node outside the winning neighbourhood. Experimental results with the N-queen problem show that bifurcations can enhance solution qualities in a consistent manner. A mechanism based on the weights' transient trajectories is proposed to account for the neural network's capacity to escape local minima.