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Noisy chaotic neural networks for solving combinatorial optimization problems

Chaotic simulated annealing (CSA) recently proposed by Chen and Aihara (1994) has been shown to have higher searching ability for solving combinatorial optimization problems compared to both the Hopfield-Tank approach and stochastic simulated annealing (SSA). However, CSA is not guaranteed to relax to a globally optimal solution no matter how slowly annealing takes place. In contrast, SSA is guaranteed to settle down to a global minimum with probability 1 if the temperature is reduced sufficiently slowly. In this paper, we attempt to combine the best of both worlds by proposing a new approach to simulated annealing using a noisy chaotic neural network, i.e., stochastic chaotic simulated annealing (SCSA). We demonstrate this approach with the 48-city traveling salesman problem.