Constraint-Based Local Search

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Abstract

Constraint-Based Local Search emerged in the last decade as a framework for declaratively expressing hard combinatorial optimization problems and solve them with local search techniques. It delivers tools to practitioners that enables them to quickly experiment with multiple models, heuristics, and meta-heuristics, focusing on their application rather than the delicate minutiae of producing a competitive implementation. At its heart, the declarative models are reminiscent of the modeling facilities familiar to constraint programming, while the underlying computational model heavily depends on incrementality. The net result is a platform capable of delivering competitive local search solutions at a fraction of the efforts needed with a conventional approach delivering model-and-run to local search users.

Keywords

Constraint Local search Neighborhood Synthetic search satisfaction Optimization Incremental model Declarative 
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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.University of ConnecticutStorrsUSA
  2. 2.University of MichiganAnn ArborUSA

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