Journal
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
Volume 174, Issue 3, Pages 1519-1539Publisher
ELSEVIER
DOI: 10.1016/j.ejor.2005.01.066
Keywords
iterated local search; quadratic assignment problem; run-time distributions; search space analysis
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Iterated local search (ILS) is a simple and powerful stochastic local search method. This article presents and analyzes the application of ILS to the quadratic assignment problem (QAP). We justify the potential usefulness of an ILS approach to this problem by an analysis of the QAP search space. However, an analysis of the run-time behavior of a basic ILS algorithm reveals a stagnation behavior which strongly compromises its performance. To avoid this stagnation behavior, we enhance the ILS algorithm using acceptance criteria that allow moves to worse local optima and we propose population-based ILS extensions. An experimental evaluation of the enhanced ILS algorithms shows their excellent performance when compared to other state-of-the-art algorithms for the QAP. (c) 2005 Elsevier B.V. All rights reserved.
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