Local search with constraint propagation and conflict-based heuristics

Jussien, Narendra; Lhomme, Olivier

doi:10.1016/s0004-3702(02)00221-7

Cited by 112 publications

(79 citation statements)

References 24 publications

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“…Generally, when LS is considered as the master, CP is used to improve the quality or the size of the neighborhood (Jussien and Lhomme, 2002). Other techniques sacrifice completeness.…”

Section: Hybridizing Cp and Local Search (Ls)mentioning

confidence: 99%

Hybrid strategies of enumeration in constraint solving

Monfroy¹,

Crawford²,

Soto³

et al. 2011

Sci. Res. Essays

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Enumeration strategies (that is, selection of a variable and a value of its domain) are crucial components of Constraint Programming: they significantly influence the performances of the solving process, sometimes of several orders of magnitude. In this paper, we propose to use Local Search in order to help and guide enumeration: we extend the usual variable selection strategies of constraint programming and we perform the value selection with respect to the results of some local search. The experimental results obtained are rather promising.

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“…Generally, when LS is considered as the master, CP is used to improve the quality or the size of the neighborhood (Jussien and Lhomme, 2002). Other techniques sacrifice completeness.…”

Section: Hybridizing Cp and Local Search (Ls)mentioning

confidence: 99%

Hybrid strategies of enumeration in constraint solving

Monfroy¹,

Crawford²,

Soto³

et al. 2011

Sci. Res. Essays

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“…Also, local search and genetic algorithms are used, by themselves or combined with each other and with dispatching rules: [14] presents an iterative improvement algorithm with a heuristic dispatching rule to generate the initial solutions; [15] proposes a tabu search algorithm, [2] introduces a genetic algorithm hybridised with local search, and a genetic algorithm using heuristic seeding is proposed in [16]. In [17], a local search with constraint propagation and conflict-based heuristics framework is applied to OSP, and [3] proposes a solution based on particle swarm optimisation. However, to our knowledge, none of these metaheuristic techniques have been adapted to the case where durations are fuzzy numbers.…”

Section: Genetic Algorithms For the Fospmentioning

confidence: 99%

A Genetic Algorithm for the Open Shop Problem with Uncertain Durations

Palacios

Puente

Vela

et al. 2009

Methods and Models in Artificial and Natural Computation. A Homage to Professor Mira’s Scientific Legacy

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Abstract. We consider a variation of the open shop problem where task durations are allowed to be uncertain and where uncertainty is modelled using fuzzy numbers. We propose a genetic approach to minimise the expected makespan: we consider different possibilities for the genetic operators and analyse their performance, in order to obtain a competitive configuration. Finally, the performance of the proposed genetic algorithm is tested on several benchmark problems, modified so as to have fuzzy durations, compared with a greedy heuristic from the literature.

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“…For example, Jussien and Lhomme introduced the path-repair algorithm for CSP [14], which adds domain filtering techniques and no-good learning to local search. Furthermore, Hirsch and Kojevnikov introduced the UnitWalk SAT solver [13], which combines the iterative application of the unit clause rule with local search.…”

Section: Related Workmentioning

confidence: 99%

Heuristic-Based Backtracking Relaxation for Propositional Satisfiability

Bhalla¹,

Lynce²,

Sousa³

et al. 2005

Sat 2005

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Abstract. In recent years backtrack search algorithms for propositional satisfiability (SAT) have been the subject of dramatic improvements. These improvements allowed SAT solvers to successfully solve instances with thousands or tens of thousands of variables. However, many new challenging problem instances are still too hard for current SAT solvers. As a result, further improvements to SAT technology are expected to have key consequences in solving hard realworld instances. This paper introduces a new idea: choosing the backtrack variable using a heuristic approach with the goal of diversifying the regions of the space that are explored during the search. The proposed heuristics are inspired by the heuristics proposed in recent years for the decision branching step of SAT solvers, namely, VSIDS and its improvements. Completeness conditions are established, which guarantee completeness for the new algorithm, as well as for any other incomplete backtracking algorithm. Experimental results on hundreds of instances derived from real-world problems show that the new technique is able to speed SAT solvers, while aborting fewer instances. These results clearly motivate the integration of heuristic backtracking in SAT solvers.

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Local search with constraint propagation and conflict-based heuristics

Cited by 112 publications

References 24 publications

Hybrid strategies of enumeration in constraint solving

Hybrid strategies of enumeration in constraint solving

A Genetic Algorithm for the Open Shop Problem with Uncertain Durations

Heuristic-Based Backtracking Relaxation for Propositional Satisfiability

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