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ParadisEO-MO: from fitness landscape analysis to efficient local search algorithms

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Abstract

This paper presents a general-purpose software framework dedicated to the design, the analysis and the implementation of local search metaheuristics: ParadisEO-MO. A substantial number of single solution-based local search metaheuristics has been proposed so far, and an attempt of unifying existing approaches is here presented. Based on a fine-grained decomposition, a conceptual model is proposed and is validated by regarding a number of state-of-the-art methodologies as simple variants of the same structure. This model is then incorporated into the ParadisEO-MO software framework. This framework has proven its efficiency and high flexibility by enabling the resolution of many academic and real-world optimization problems from science and industry.

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Acknowledgments

The authors would like to gratefully acknowledge the reviewers for their valuable feedback that highly contributed to improve the quality of the paper. Moreover, we would like to thank the Inria research institute for their support on the DOLPHIN project. Thanks are also due to all the members of the DOLPHIN research group for their collaboration in the development of the ParadisEO framework.

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Authors and Affiliations

  1. Département IA, École des Mines de Douai, 941 Rue Charles Bourseul, BP 10838, Douai, 59508, France

    J. Humeau

  2. Inria Lille-Nord Europe, DOLPHIN Research Team, Parc Scientifique de la Haute Borne, 40 Avenue Halley, Villeneuve d’Ascq, 59650, France

    A. Liefooghe, E. -G. Talbi & S. Verel

  3. Laboratoire LIFL, UMR CNRS 8022, Université Lille 1, Cité Scientifique, Bât. M3, Villeneuve d’Ascq Cedex, 59655, France

    A. Liefooghe & E. -G. Talbi

  4. Laboratoire I3S, UMR CNRS 6070, Université Nice Sophia Antipolis, 2000 Route Des Lucioles, BP 121, Sophia Antipolis Cedex, 06903, France

    S. Verel

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  1. J. Humeau
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Correspondence to A. Liefooghe.

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Humeau, J., Liefooghe, A., Talbi, E.G. et al. ParadisEO-MO: from fitness landscape analysis to efficient local search algorithms. J Heuristics 19, 881–915 (2013). https://doi.org/10.1007/s10732-013-9228-8

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