alexa A noisy chaotic neural network for solving combinatorial optimization problems: stochastic chaotic simulated annealing
Engineering

Engineering

Journal of Information Technology & Software Engineering

Author(s): Lipo Wang, Sa Li, F Tian, Xiuju Fu

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Recently Chen and Aihara have demonstrated both experimentally and mathematically that their chaotic simulated annealing (CSA) has better search ability for solving combinatorial optimization problems compared to both the Hopfield-Tank approach and stochastic simulated annealing (SSA). However, CSA may not find a globally optimal solution no matter how slowly annealing is carried out, because the chaotic dynamics are completely deterministic. In contrast, SSA tends to settle down to a global optimum if the temperature is reduced sufficiently slowly. Here we combine the best features of both SSA and CSA, thereby proposing a new approach for solving optimization problems, i.e., stochastic chaotic simulated annealing, by using a noisy chaotic neural network. We show the effectiveness of this new approach with two difficult combinatorial optimization problems, i.e., a traveling salesman problem and a channel assignment problem for cellular mobile communications.

This article was published in IEEE Systems, Man, and Cybernetics Society and referenced in Journal of Information Technology & Software Engineering

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