Combining constraint propagation and meta-heuristics for searching a maximum weight hamiltonian chain
RAIRO - Operations Research - Recherche Opérationnelle, Tome 40 (2006) no. 2, pp. 77-95.

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.

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     author = {Caseau, Yves},
     title = {Combining constraint propagation and meta-heuristics for searching a maximum weight hamiltonian chain},
     journal = {RAIRO - Operations Research - Recherche Op\'erationnelle},
     pages = {77--95},
     publisher = {EDP-Sciences},
     volume = {40},
     number = {2},
     year = {2006},
     doi = {10.1051/ro:2006018},
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Caseau, Yves. Combining constraint propagation and meta-heuristics for searching a maximum weight hamiltonian chain. RAIRO - Operations Research - Recherche Opérationnelle, Tome 40 (2006) no. 2, pp. 77-95. doi : 10.1051/ro:2006018. http://www.numdam.org/articles/10.1051/ro:2006018/

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