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An Improved Chaotic Maximum Neural Network for Maximum Clique Problem


Gang Yang, Zheng Tang, Junyan Yi


Vol. 7  No. 2  pp. 1-7


We propose an improved chaotic maximum neural network to solve maximum clique problem. Through analyzing the character of maximum neural network with an added vertex in maximum clique problem, we find that the quality and size of clique solution can be modified by tuning a parameter about vertex weight. Based on the analysis, a random nonlinear self-feedback and flexible annealing strategy are embedded in maximum neural network, which makes the network more powerful to escape local minima and be independent of the initial values. The simulation in k random graph and some graphs of the DIMACS clique instances in the second DIMACS challenge shows that our improved network is superior to other algorithms in light of the solution quality and CPU time.


Neural network, Maximum neural network, Maximum clique Problem, Annealing strategy