Noisy chaotic neural networks for solving combinatorial optimization problems
Lipo Wang, Fuyu Tian · 2000
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.