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A Noisy Chaotic Neural Network for Solving Combinatorial Optimization Problems: Stochastic Chaotic Simulated Annealing.
- Source :
- IEEE Transactions on Systems, Man & Cybernetics: Part B; Oct2004, Vol. 34 Issue 5, p2119-2125, 7p
- Publication Year :
- 2004
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10834419
- Volume :
- 34
- Issue :
- 5
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Systems, Man & Cybernetics: Part B
- Publication Type :
- Academic Journal
- Accession number :
- 14636007
- Full Text :
- https://doi.org/10.1109/TSMCB.2004.829778