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Combining constraint Propagation and meta-heuristics for searching a Maximum Weight Hamiltonian Chain

Published online by Cambridge University Press:  12 October 2006

Yves Caseau
Yves Caseau*
Affiliation:
Bouygues e-Lab, 1 avenue Eugène Freyssinet, 78061 St-Quentin en Yvelines Cedex, France; e-mail: ycs@caseau.com
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Abstract

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 [1], which can be generated or tuned automatically using a learning meta-strategy [2]. In this paper, the hybrid combination that is investigated mixes constraint propagation, a special form of limited discrepancy search and large neighborhood search.

Type
Research Article
Information
RAIRO - Operations Research , Volume 40 , Issue 2: ROADEF 03 , April 2006 , pp. 77 - 95
Copyright
© EDP Sciences, 2006

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References

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