ParadisEO-MO: From Fitness Landscape Analysis to Efficient Local Search Algorithms

Jérémie Humeau (1) , Arnaud Liefooghe (2, 3) , El-Ghazali Talbi (3, 2) , Sébastien Verel (2, 4)

1 CERI SN - IMT Nord Europe - Centre for Digital Systems
2 DOLPHIN - Parallel Cooperative Multi-criteria Optimization
3 LIFL - Laboratoire d'Informatique Fondamentale de Lille
4 Laboratoire d'Informatique, Signaux, et Systèmes de Sophia-Antipolis (I3S) / Groupe SCOBI

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.

Domaines: Recherche opérationnelle [math.OC]

Jérémie Humeau, Arnaud Liefooghe, El-Ghazali Talbi, Sébastien Verel. ParadisEO-MO: From Fitness Landscape Analysis to Efficient Local Search Algorithms. Journal of Heuristics, 2013, 19 (6), pp.881-915. ⟨10.1007/s10732-013-9228-8⟩. ⟨hal-00832029⟩