Journal of Heuristics

, Volume 19, Issue 6, pp 881–915 | Cite as

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

Article

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.

Keywords

Local search Metaheuristic Fitness landscapes  Conceptual unified model Algorithm design and analysis Software framework  

Notes

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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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • J. Humeau
    • 1
  • A. Liefooghe
    • 2
    • 3
  • E. -G. Talbi
    • 2
    • 3
  • S. Verel
    • 2
    • 4
  1. 1.Département IAÉcole des Mines de DouaiDouaiFrance
  2. 2.Inria Lille-Nord EuropeDOLPHIN Research TeamVilleneuve d’AscqFrance
  3. 3.Laboratoire LIFL, UMR CNRS 8022Université Lille 1Villeneuve d’Ascq CedexFrance
  4. 4.Laboratoire I3S, UMR CNRS 6070Université Nice Sophia AntipolisSophia Antipolis CedexFrance

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