Presentation 2011-01-24
Effectiveness of Combinatorial Optimization Algorithms using Chaotic Dynamics which has Negative Autocorrelation
Mikio HASEGAWA,
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Abstract(in English) This paper proposes a combinatorial optimization method, which utilizes ideal asynchronous spatiotemporal chaotic dynamics for solution search in a high dimensional solution space. Such chaotic dynamics is generated by the Lebesgue spectrum filter, which has been applied to the chaotic CDMA in previous researches to minimize the cross-correlation among the sequences. In the proposed method, such a filter is applied to the output functions of optimization neural networks to realize an ideal chaotic search, which generates ideally complicated searching dynamics. The proposed scheme is applied to two combinatorial optimization approaches, the Hopfield-Tank neural network with additive noise and a simple heuristic 2-opt algorithm, which solve the Traveling Salesman Problems and the Quadratic Assignment Problems. The simulation results show that the proposed approach using the ideal chaotic dynamics simply improves the performance of the chaotic algorithms without searching appropriate parameter values even for large-scale problems.
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Keyword(in English) Combinatorial Optimization / Chaos / Neural Networks / Nonlinear Dynamics / Rebesgue Spectrum Filter
Paper # NLP2010-134,NC2010-98
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Committee NC
Conference Date 2011/1/17(1days)
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Registration To Neurocomputing (NC)
Language JPN
Title (in Japanese) (See Japanese page)
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Title (in English) Effectiveness of Combinatorial Optimization Algorithms using Chaotic Dynamics which has Negative Autocorrelation
Sub Title (in English)
Keyword(1) Combinatorial Optimization
Keyword(2) Chaos
Keyword(3) Neural Networks
Keyword(4) Nonlinear Dynamics
Keyword(5) Rebesgue Spectrum Filter
1st Author's Name Mikio HASEGAWA
1st Author's Affiliation Department of Electrical Engineering, Tokyo University of Science()
Date 2011-01-24
Paper # NLP2010-134,NC2010-98
Volume (vol) vol.110
Number (no) 388
Page pp.pp.-
#Pages 6
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