Constraint-Based Local Search. The ubiquity of combinatorial optimization problems in our society is illustrated by the novel application areas for optimization technology, which range from supply chain management to sports tournament scheduling. Over the last two decades, constraint programming has emerged as a fundamental methodology to solve a variety of combinatorial problems, and rich constraint programming languages have been developed for expressing and combining constraints and specifying search procedures at a high level of abstraction. Local search approaches to combinatorial optimization are able to isolate optimal or near-optimal solutions within reasonable time constraints. This book introduces a method for solving combinatorial optimization problems that combines constraint programming and local search, using constraints to describe and control local search, and a programming language, COMET, that supports both modeling and search abstractions in the spirit of constraint programming. After an overview of local search including neighborhoods, heuristics, and metaheuristics, the book presents the architecture and modeling and search components of constraint-based local search and describes how constraint-based local search is supported in COMET. The book describes a variety of applications, arranged by meta-heuristics. It presents scheduling applications, along with the background necessary to understand these challenging problems. The book also includes a number of satisfiability problems, illustrating the ability of constraint-based local search approaches to cope with both satisfiability and optimization problems in a uniform fashion.

This software is also peer reviewed by journal TOMS.

References in zbMATH (referenced in 80 articles , 1 standard article )

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  1. Escamocher, Guillaume; O’Sullivan, Barry: Leprechauns on the chessboard (2021)
  2. Michel, L.; Schaus, P.; Van Hentenryck, P.: MiniCP: a lightweight solver for constraint programming (2021)
  3. Tsoupidi, Rodothea Myrsini; Lozano, Roberto Castañeda; Baudry, Benoit: Constraint-based diversification of JOP gadgets (2021)
  4. Weiner, Jake; Ernst, Andreas T.; Li, Xiaodong; Sun, Yuan; Deb, Kalyanmoy: Solving the maximum edge disjoint path problem using a modified Lagrangian particle swarm optimisation hybrid (2021)
  5. Chen, Qian Matteo; Finzi, Alberto; Mancini, Toni; Melatti, Igor; Tronci, Enrico: MILP, pseudo-Boolean, and OMT solvers for optimal fault-tolerant placements of relay nodes in mission critical wireless networks (2020)
  6. Qin, Tianbao; Du, Yuquan; Chen, Jiang Hang; Sha, Mei: Combining mixed integer programming and constraint programming to solve the integrated scheduling problem of container handling operations of a single vessel (2020)
  7. Ślażyński, Mateusz: Research report on automatic synthesis of local search neighborhood operators (2019)
  8. Ślażyński, Mateusz; Abreu, Salvador; Nalepa, Grzegorz J.: Generating local search neighborhood with synthesized logic programs (2019)
  9. Gent, Ian P.; Miguel, Ian; Nightingale, Peter; McCreesh, Ciaran; Prosser, Patrick; Moore, Neil C. A.; Unsworth, Chris: A review of literature on parallel constraint solving (2018)
  10. Adamo, Tommaso; Ghiani, Gianpaolo; Grieco, Antonio; Guerriero, Emanuela; Manni, Emanuele: MIP neighborhood synthesis through semantic feature extraction and automatic algorithm configuration (2017)
  11. Guns, Tias; Dries, Anton; Nijssen, Siegfried; Tack, Guido; De Raedt, Luc: MiningZinc: a declarative framework for constraint-based mining (2017)
  12. Michel, L.; Van Hentenryck, P.: A microkernel architecture for constraint programming (2017)
  13. Umetani, Shunji: Exploiting variable associations to configure efficient local search algorithms in large-scale binary integer programs (2017)
  14. Cire, Andre A.; Hooker, John N.; Yunes, Tallys: Modeling with metaconstraints and semantic typing of variables (2016)
  15. Shishmarev, Maxim; Mears, Christopher; Tack, Guido; Garcia de la Banda, Maria: Visual search tree profiling (2016)
  16. Björdal, Gustav; Monette, Jean-Noël; Flener, Pierre; Pearson, Justin: A constraint-based local search backend for MiniZinc (2015)
  17. Caniou, Yves; Codognet, Philippe; Richoux, Florian; Diaz, Daniel; Abreu, Salvador: Large-scale parallelism for constraint-based local search: the costas array case study (2015)
  18. Sörensen, Kenneth: Metaheuristics -- the metaphor exposed (2015)
  19. Schrijvers, Tom; Demoen, Bart; Triska, Markus; Desouter, Benoit: \textscTor: modular search with hookable disjunction (2014)
  20. Abdallah, A. Nait; van Emden, M. H.: Constraint propagation as information maximization (2013)

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