Noisy Chaotic Neural Networks for Solving Combinatorial Optimization Problems

Authors:
Fuyu Tian
Affiliations:
-
Venue:
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 4 - Volume 4
Year:
2000

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Abstract

Chaotic simulated annealing (CSA) recently proposed by Chen and Aihara 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.