The frequency assignment problem belongs to the quite difficult to deal with class of NP (nondeterministic polynomian)-hard cominatorial optimiszation problems [3]. Its computational complexity directs researchers in the field at developing efficient techniques for finding solutions realizing minimum (or maximum) values of an objective function subject to a set of, often conflicting, constraints. To seek an optimal (or near optimal) solution, many methods have been proposed, such as dynamic programming methods, branch and bound methods, etc., and, lately, some heuristic algorothms relating to physical and biological phenomena. They include tabu search, genetic algorithms, simulated annealing and artificial neural networks [4]. We propose a Hopfield neural network model with chaotic neurodynamics to overcome the obstacle of local minima in the energy function and obtain optimal solutions in less iteration than the time-consuming converget dynamics.
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