An efficient graph coloring algorithm by merging a rapid strategy into a transiently chaotic neural network with hysteretic output function

Abstract

In this paper an efficient graph coloring algorithm based on the transiently chaotic neural network (TCNN) is presented. This algorithm apply the TCNN with hysteretic output function instead of logistic output function, this make the model has higher ability of overcoming drawbacks that suffer from the local minimum. Meanwhile, a rapid strategy is merged in this model in order to avoid oscillation and offer a considerable acceleration of converging to the optimal solution. The numerical simulation results demonstrated that the proposed model has higher ability and more rapid speed to search for globally optimal solution of the graph coloring problem than the previous TCNN model with logistic output function and without the rapid strategy. © 2011 Springer-Verlag.