A constraint-based local search backend for MiniZinc

Björdal, Gustav; Monette, Jean-Noël; Flener, Pierre; Pearson, Justin

doi:10.1007/s10601-015-9184-z

Cited by 20 publications

(10 citation statements)

References 22 publications

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“…In particular, it introduces the concept of incremental variable allowing an incremental evaluation of neighbourhoods, and uses invariants that are stated declaratively to achieve this. In [Björdal et al, 2015], a CBLS backend is described for the MiniZinc CSP modelling language. Other generic systems of CSP solving based on local search are presented in [Davenport et al, 1994;Nonobe and Ibaraki, 1998;Codognet and Diaz, 2001;Galinier and Hao, 2004].…”

Section: Local Searchmentioning

confidence: 99%

Meta-heuristics and Artificial Intelligence

Hao

Solnon

2020

A Guided Tour of Artificial Intelligence Research

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Meta-heuristics are generic search methods that are used to solve challenging combinatorial problems. We describe these methods and highlight their common features and differences by grouping them in two main kinds of approaches: Perturbative meta-heuristics that build new combinations by modifying existing combinations (such as, for example, genetic algorithms and local search), and Constructive meta-heuristics that generate new combinations in an incremental way by using a stochastic model (such as, for example, estimation of distribution algorithms and ant colony optimization). These approaches may be hybridised, and we describe some classical hybrid schemes. We also introduce the notions of diversification (exploration) and intensification (exploitation), which are shared by all these metaheuristics: diversification aims at ensuring a good sampling of the search space and, therefore, at reducing the risk of ignoring a promising sub-area which actually contains high-quality solutions, whereas intensification aims at locating the best combinations within a limited region of the search space. Finally, we describe two applications of meta-heuristics to typical artificial intelligence problems: satisfiability of Boolean formulas, and constraint satisfaction problems.

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Section: Local Searchmentioning

confidence: 99%

Meta-heuristics and Artificial Intelligence

Hao

Solnon

2020

A Guided Tour of Artificial Intelligence Research

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“…the alldifferent constraint. This approach was further proclaimed in [8] and implemented by the Yuck [12] and OscaR/CBLS [3] solvers, providing dedicated neighborhoods to handle specific global constraints. Recently, a system was proposed which infers plausible neighborhood operators from data structures occurring in the Constraint Programming model, as defined in the Essence language [1].…”

Section: Related Workmentioning

confidence: 99%

Generating Local Search Neighborhood with Synthesized Logic Programs

Ślażyński¹,

Abreu

Nalepa³

2019

Electron. Proc. Theor. Comput. Sci.

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Local Search meta-heuristics have been proven a viable approach to solve difficult optimization problems. Their performance depends strongly on the search space landscape, as defined by a cost function and the selected neighborhood operators. In this paper we present a logic programming based framework, named Noodle, designed to generate bespoke Local Search neighborhoods tailored to specific discrete optimization problems. The proposed system consists of a domain specific language, which is inspired by logic programming, as well as a genetic programming solver, based on the grammar evolution algorithm. We complement the description with a preliminary experimental evaluation, where we synthesize efficient neighborhood operators for the traveling salesman problem, some of which reproduce well-known results.

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“…MiniSearch [25] allows to mix LNS with any solver supporting the MiniZinc [19] language. Yuck [17] and OscaR/CBLS [5] provide dedicated neighborhoods to handle specific global constraints.…”

Section: Background and Overview Of The Existing Literaturementioning

confidence: 99%

Research Report on Automatic Synthesis of Local Search Neighborhood Operators

Ślażyński

2019

Electron. Proc. Theor. Comput. Sci.

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Constraint Programming (CP) and Local Search (LS) are different paradigms for dealing with combinatorial search and optimization problems. Their complementary features motivated researchers to create hybrid CP/LS solutions, maintaining both the modeling capabilities of CP and the computational advantages of the heuristic-based LS approach. Research presented in this report is focused on developing a novel method to infer an efficient LS neighborhood operator based on the problem structure, as modeled in the CP paradigm. We consider a limited formal language that we call a Neighborhood Definition Language, used to specify the neighborhood operators in a fine-grained and declarative manner. Together with Logic Programming runtime called Noodle, it allows to automatically synthesize complex operators using a Grammar Evolution algorithm.

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A constraint-based local search backend for MiniZinc

Cited by 20 publications

References 22 publications

Meta-heuristics and Artificial Intelligence

Meta-heuristics and Artificial Intelligence

Generating Local Search Neighborhood with Synthesized Logic Programs

Research Report on Automatic Synthesis of Local Search Neighborhood Operators

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