Investigating the landscape of a hybrid local search approach for a timetabling problem

Thomas Feutrier (1, 2) , Marie-Éléonore Kessaci (2, 1) , Nadarajen Veerapen (2, 1)

1 CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189
2 ORKAD - Operational Research, Knowledge And Data

Curriculum-Based Course Timetabling is an NP-hard problem that can be efficiently solved by metaheuristics. The International Timetabling Competition (ITC) 2007 was won by a hybrid local search (HLS) combining Hill Climbing, Great Deluge and Simulated Annealing. HLS remains one of the best local search algorithms to solve this problem. In this paper, we investigate the search landscape of 21 instances to analyze the behavior of the HLS components. We also propose a new distance metric that aims to be more robust and be less influenced by symmetry. Experiments show that the HLS and the embedded simulated annealing have the same general behavior but HLS leads to better robustness. This analysis strongly suggests that the HLS components and/or parameter values should be automatically configured to further improve performance. CCS CONCEPTS • Computing methodologies → Discrete space search.

Mots clés: University timetabling; landscape analysis; local search

Domaines: Mathématique discrète [cs.DM]; Recherche opérationnelle [math.OC]

Thomas Feutrier, Marie-Éléonore Kessaci, Nadarajen Veerapen. Investigating the landscape of a hybrid local search approach for a timetabling problem. GECCO '21 Companion: Companion Conference on Genetic and Evolutionary Computation, ACM, Jul 2021, Lille, France. pp.1665-1673, ⟨10.1145/3449726.3463175⟩. ⟨hal-03509996⟩