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A Filter-and-Fan Metaheuristic for the 0-1 Multidimensional Knapsack Problem

Author

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  • Mahdi Khemakhem

    (LOGIQ – ISGI, University of Sfax, Sfax, Tunisia)

  • Boukthir Haddar

    (LOGIQ – ISGI, University of Sfax, Sfax, Tunisia)

  • Khalil Chebil

    (LOGIQ – ISGI, University of Sfax, Sfax, Tunisia)

  • Saïd Hanafi

    (LAMIH, University of Valenciennes, Valenciennes, France)

Abstract

This paper proposes a new hybrid tree search algorithm to the Multidimensional Knapsack Problem (MKP) that effectively combines tabu search with a dynamic and adaptive neighborhood search procedure. The authors’ heuristic, based on a filter-and-fan (F&F) procedure, uses a Linear Programming-based Heuristic to generate a starting solution to the F&F process. A tabu search procedure is used to try to enhance the best solution value provided by the F&F method that generates compound moves by a strategically truncated form of tree search. They report the first application of the F&F method to the MKP. Experimental results obtained on a wide set of benchmark problems clearly demonstrate the competitiveness of the proposed method compared to the state-of-the-art heuristic methods.

Suggested Citation

  • Mahdi Khemakhem & Boukthir Haddar & Khalil Chebil & Saïd Hanafi, 2012. "A Filter-and-Fan Metaheuristic for the 0-1 Multidimensional Knapsack Problem," International Journal of Applied Metaheuristic Computing (IJAMC), IGI Global, vol. 3(4), pages 43-63, October.
    • Handle: RePEc:igg:jamc00:v:3:y:2012:i:4:p:43-63
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    Cited by:

    1. Pierre Hansen & Nenad Mladenović & Raca Todosijević & Saïd Hanafi, 2017. "Variable neighborhood search: basics and variants," EURO Journal on Computational Optimization, Springer;EURO - The Association of European Operational Research Societies, vol. 5(3), pages 423-454, September.

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