λ-Opt neural approaches to quadratic assignment problems

Shin Ishii, Hirotaka Niitsuma

    Research output: Contribution to journalArticlepeer-review

    4 Citations (Scopus)

    Abstract

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

    Original languageEnglish
    Pages (from-to)2209-2225
    Number of pages17
    JournalNeural Computation
    Volume12
    Issue number9
    DOIs
    Publication statusPublished - Sept 2000

    ASJC Scopus subject areas

    • Arts and Humanities (miscellaneous)
    • Cognitive Neuroscience

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