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Combining simulated annealing with local search heuristics

  • Simulated Annealing
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Abstract

We introduce a meta-heuristic to combine simulated annealing with local search methods for CO problems. This new class of Markov chains leads to significantly more powerful optimization methods than either simulated annealing or local search. The main idea is to embed deterministic local search techniques into simulated annealing so that the chain explores only local optima. It makes large, global changes, even at low temperatures, thus overcoming large barriers in configuration space. We have tested this meta-heuristic for the traveling salesman and graph partitioning problems. Tests on instances from public libraries and random ensembles quantify the power of the method. Our algorithm is able to solve large instances to optimality, improving upon local search methods very significantly. For the traveling salesman problem with randomly distributed cities, in a square, the procedure improves on 3-opt by 1.6%, and on Lin-Kernighan local search by 1.3%. For the partitioning of sparse random graphs of average degree equal to 5, the improvement over Kernighan-Lin local search is 8.9%. For both CO problems, we obtain new best heuristics.

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Author notes

  1. Olivier C. Martin

    Present address: Unité de Recherche des Universités Paris XI et Paris VI associée au C.N.R.S., Paris, La France

Authors and Affiliations

  1. Division de Physique Théorique, Institut de Physique Nucléaire, F-91406, Orsay Cedex, France

    Olivier C. Martin

  2. Department of Computer Science and Engineering, Oregon Graduate Institute of Science and Technology, 20000 NW Walker Road, P.O. Box 91000, 97291-1000, Portland, OR, USA

    Steve W. Otto

Authors
  1. Olivier C. Martin
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Martin, O.C., Otto, S.W. Combining simulated annealing with local search heuristics. Ann Oper Res 63, 57–75 (1996). https://doi.org/10.1007/BF02601639

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