Applying an Extended Guided Local Search to the Quadratic Assignment Problem

Patrick Mills (), Edward Tsang () and John Ford ()

Annals of Operations Research, 2003, vol. 118, issue 1, 135 pages

Abstract: In this paper, we show how an extended Guided Local Search (GLS) can be applied to the Quadratic Assignment Problem (QAP). GLS is a general, penalty-based meta-heuristic, which sits on top of local search algorithms, to help guide them out of local minima. We present empirical results of applying several extended versions of GLS to the QAP, and show that these extensions can improve the range of parameter settings within which Guided Local Search performs well. Finally, we compare the results of running our extended GLS with some state of the art algorithms for the QAP. Copyright Kluwer Academic Publishers 2003

Keywords: local search; meta-heuristics; quadratic assignment problem (search for similar items in EconPapers)
Date: 2003
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DOI: 10.1023/A:1021857607524

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