Analog λ-opt approach to quadratic assignment problem

Hirotaka Niitsuma, Shin Ishii, Minoru Ito

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Abstract

In this paper, we propose a novel analog neural approach to combinatorial optimization problems, in particular, the quadratic assignment problem (QAP). Our proposed method is 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 method can achieve a middle-range search over the possible solutions, and this helps the system neglect shallow local minima and escape from local minima. In experiments, we have applied our method to relatively large-scale (N = 80 to 150) QAPs. Results have shown that our new method is comparable to the present champion algorithms, and for two benchmark problems, it is able to obtain better solutions than the previous champion algorithms.

Original languageEnglish
Pages (from-to)1-9
Number of pages9
JournalSystems and Computers in Japan
Volume31
Issue number10
DOIs
Publication statusPublished - Sep 1 2000

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ASJC Scopus subject areas

  • Theoretical Computer Science
  • Information Systems
  • Hardware and Architecture
  • Computational Theory and Mathematics

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