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Improving local-search metaheuristics through look-ahead policies

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Abstract

As a basic principle, look-ahead approaches investigate the outcomes of potential future steps to evaluate the quality of alternative search directions. Different policies exist to set up look-ahead methods differing in the object of inspection and in the extensiveness of the search. In this work, two original look-ahead strategies are developed and tested through numerical experiments. The first method introduces a look-ahead mechanism that acts as a hyper-heuristic for comparing and selecting local-search operators. The second method uses a look-ahead strategy on a lower level in order to guide a local-search metaheuristic. The proposed approaches are implemented using a hyper-heuristic framework. They are tested against alternative methods using two different competition benchmarks, including a comparison with results given in literature. Furthermore, in a second set of experiments, a detailed investigation regarding the influence of particular parameter values is executed for one method. The experiments reveal that the inclusion of a simple look-ahead principle into an iterated local-search procedure significantly improves the outcome regarding the considered benchmarks.

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Authors and Affiliations

  1. Department of Mathematics and Computer Science, University of Osnabrück, Albrechtstraße 28, 49069, Osnabrück, Germany

    David Meignan

  2. Institute of Information Systems, University of Hamburg, Von-Melle-Park 5, 20146, Hamburg, Germany

    Silvia Schwarze & Stefan Voß

  3. Escuela de Ingeniería Industrial, Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile

    Stefan Voß

Authors
  1. David Meignan
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  2. Silvia Schwarze
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  3. Stefan Voß
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Correspondence to Silvia Schwarze.

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Meignan, D., Schwarze, S. & Voß, S. Improving local-search metaheuristics through look-ahead policies. Ann Math Artif Intell 76, 59–82 (2016). https://doi.org/10.1007/s10472-015-9453-y

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