Solving large scale combinatorial optimization problems based on a divide and conquer strategy

DOI10.1007/BF01414168zbMath0911.90224OpenAlexW2001050444MaRDI QIDQ1271691

I. I. Esat, T. J. Suh

Publication date: 5 May 1999

Published in: Neural Computing and Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf01414168

zbMATH Keywords

Hopfield neural networklarge scale optimizationdivide and conquerGrossberg regularity detectorsequence-dependent set-up time minimization

Mathematics Subject Classification ID

Large-scale problems in mathematical programming (90C06) Learning and adaptive systems in artificial intelligence (68T05) Deterministic scheduling theory in operations research (90B35) Combinatorial optimization (90C27) Pattern recognition, speech recognition (68T10)

Related Items (1)

Improving the Hopfield model performance when applied to the traveling salesman problem. A divide-and-conquer scheme

Cites Work

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• ``Neural computation of decisions in optimization problems
• On the stability of the travelling salesman problem algorithm of Hopfield and Tank
• Adaptive pattern classification and universal recoding. II: Feedback, expectation, olfaction, illusions
• A dynamic programming formulation for the one machine sequencing problem
• On problem solving with Hopfield neural networks

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