λ-opt neural networks for quadratic assignment problem

Shin Ishii, Hirotaka Niitsuma

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We propose new analog neural approaches to quadratic assignment problems. Our methods are based on an analog version of the λ-opt heuristics, which simultaneously changes assignments for λ elements in a permutation. Since we can take a relatively large λ value, our methods can achieve a middle-range search over the possible solutions, and this helps the system neglect shallow local minima and escape from local minima. Results have shown that our methods are comparable to the present champion algorithms, and for two benchmark problems, they are able to obtain better solutions than the previous champion algorithms.

Original languageEnglish
Title of host publicationIEE Conference Publication
PublisherIEE
Pages115-120
Number of pages6
Edition470
ISBN (Print)0852967217
Publication statusPublished - Dec 1 1999
EventProceedings of the 1999 the 9th International Conference on 'Artificial Neural Networks (ICANN99)' - Edinburgh, UK
Duration: Sep 7 1999Sep 10 1999

Publication series

NameIEE Conference Publication
Number470
Volume1
ISSN (Print)0537-9989

Other

OtherProceedings of the 1999 the 9th International Conference on 'Artificial Neural Networks (ICANN99)'
CityEdinburgh, UK
Period9/7/999/10/99

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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  • Cite this

    Ishii, S., & Niitsuma, H. (1999). λ-opt neural networks for quadratic assignment problem. In IEE Conference Publication (470 ed., pp. 115-120). (IEE Conference Publication; Vol. 1, No. 470). IEE.