Combining constraint Propagation and meta-heuristics for searching a Maximum Weight Hamiltonian Chain
Bouygues e-Lab, 1 avenue Eugène Freyssinet,
78061 St-Quentin en Yvelines Cedex, France;
This paper presents the approach that we developed to solve the ROADEF 2003 challenge problem. This work is part of a research program whose aim is to study the benefits and the computer-aided generation of hybrid solutions that mix constraint programming and meta-heuristics, such as large neighborhood search (LNS). This paper focuses on three contributions that were obtained during this project: an improved method for propagating Hamiltonian chain constraints, a fresh look at limited discrepancy search and the introduction of randomization and de-randomization within our combination algebra. This algebra is made of terms that represent optimization algorithms, following the approach of SALSA , which can be generated or tuned automatically using a learning meta-strategy . In this paper, the hybrid combination that is investigated mixes constraint propagation, a special form of limited discrepancy search and large neighborhood search.
© EDP Sciences, 2006