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Constrained neural approaches to quadratic assignment problems.

S Ishii1, M Sato

  • 1Nara Institute of Science and Technology, 8916-5 Takayama-cho, Ikoma-shi, Nara, Japan

Neural Networks : the Official Journal of the International Neural Network Society
|March 29, 2003
PubMed
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Analog neural networks offer improved solutions for the quadratic assignment problem (QAP). These novel approaches provide effective alternatives for finding good solutions quickly, even for large-scale problems.

Area of Science:

  • Computational intelligence
  • Operations research
  • Artificial neural networks

Background:

  • The quadratic assignment problem (QAP) is a complex combinatorial optimization problem.
  • Existing heuristics for QAP are limited, necessitating new solution methodologies.

Purpose of the Study:

  • To introduce and evaluate analog neural network approaches for solving the QAP.
  • To demonstrate the efficacy of hard constraints in enhancing QAP solutions.

Main Methods:

  • Development of analog neural network models incorporating a hard constraints scheme.
  • Comparative analysis against conventional neural approaches and existing heuristics.

Main Results:

  • Analog neural approaches significantly improve solution quality for QAP.

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  • These methods achieve good solutions rapidly, outperforming conventional techniques.
  • Scalability demonstrated for large problem instances (N>=300).

    • Conclusions:

    • Analog neural networks with hard constraints present a powerful alternative for QAP.
    • The proposed methods offer efficient and effective solutions for both small and large-scale QAP instances.