Presentation 2013-10-28
Solution Decision Method of Quadratic Assignment Problem from Mutually Connected Chaotic Neural Network
Takafumi MATSUURA, Tohru IKEGUCHI,
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Abstract(in English) The quadratic assignment problem (QAP) is one of famous combinatorial optimization problems which belong to a class of NP-hard. To solve the QAP, a chaotic search method which uses mutually-connected chaotic neural network (CNN) has already been proposed. In the method, chaotic dynamics of the CNN effectively controls to avoid the local minima and to search optimal or near-optimal solutions. However, it is not so easy to generate feasible solutions from states of the CNN, because an output of a chaotic neuron takes an analog value. Thus, for obtaining good solutions from the CNN, it is important to develop a solution decision method that always generates a feasible solution of the QAP. To generate a good feasible solution of the QAP, we have already reported that solutions of the QAP should be decided so that a sum of value of the internal state of neurons which construct a solution of the QAP is maximized. In this paper, to improve performances of the CNN, we propose an improve method to maximize the sum of internal states of the selected chaotic neurons.
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Keyword(in English) quadratic assignment problem / chaotic neural network / chaotic neuron / heuristics method
Paper # NLP2013-83
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Conference Information
Committee NLP
Conference Date 2013/10/21(1days)
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Paper Information
Registration To Nonlinear Problems (NLP)
Language ENG
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Solution Decision Method of Quadratic Assignment Problem from Mutually Connected Chaotic Neural Network
Sub Title (in English)
Keyword(1) quadratic assignment problem
Keyword(2) chaotic neural network
Keyword(3) chaotic neuron
Keyword(4) heuristics method
1st Author's Name Takafumi MATSUURA
1st Author's Affiliation Graduate School of Engineering, Tokyo University of Science()
2nd Author's Name Tohru IKEGUCHI
2nd Author's Affiliation Graduate School of Science and Engineering, Saitama University:Saitama Uneversity Brain Science Institute
Date 2013-10-28
Paper # NLP2013-83
Volume (vol) vol.113
Number (no) 271
Page pp.pp.-
#Pages 6
Date of Issue