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. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | quadratic assignment problem / chaotic neural network / chaotic neuron / heuristics method |
Paper # | NLP2013-83 |
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Committee | NLP |
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Conference Date | 2013/10/21(1days) |
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Registration To | Nonlinear Problems (NLP) |
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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 |