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|Title:||A noisy chaotic neural network for solving combinatorial optimization problems : stochastic chaotic simulated annealing||Authors:||Wang, Lipo.
|Keywords:||DRNTU::Engineering::Electrical and electronic engineering||Issue Date:||2004||Source:||Wang, L., Li, S., Tian, F., & Fu, X. (2004). A noisy chaotic neural network for solving combinatorial optimization problems: stochastic chaotic simulated annealing. IEEE Transactions on Systems, Man and Cybernetics-Part B: Cybernetics, 34(5), 2119-2125.||Series/Report no.:||IEEE transactions on systems, man and cybernetics-Part B: cybernetics||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.||URI:||https://hdl.handle.net/10356/93979
|DOI:||10.1109/TSMCB.2004.829778||Rights:||© 2004 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://dx.doi.org/10.1109/TSMCB.2004.829778].||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||EEE Journal Articles|
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