Parallel Local Search

Codognet, Philippe; Munera, Danny; Díaz, Daniel; Abreu, Salvador

doi:10.1007/978-3-319-63516-3_10

Cited by 8 publications

(5 citation statements)

References 88 publications

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“…Solvers exist for both propagation-based search and metaheuristic search, which exhibit high performance and the capacity to make use of parallel hardware to attain yet better performance, e.g. as discussed in [ 17,18].…”

Section: Constraint Programmingmentioning

confidence: 99%

Multi-objective Finite-Domain Constraint-Based Forest Management

Eloy,

Bushenkov,

Abreu

2023

Springer Proceedings in Mathematics &Amp; Statistics

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This paper describes an implementation of a Constraint Programming approach to the problem of multi-criteria forest management optimization. The goal is to decide when to harvest each forest unit while striving to optimize several criteria under spatial restrictions. With a large number of management units, the optimization problem becomes computationally intractable. We propose an approach for deriving a set of efficient solutions for the entire region. The proposed methodology was tested for Vale do Sousa region in the North of Portugal.

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Section: Constraint Programmingmentioning

confidence: 99%

Multi-objective Finite-Domain Constraint-Based Forest Management

Eloy,

Bushenkov,

Abreu

2023

Springer Proceedings in Mathematics &Amp; Statistics

Self Cite

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“…In order to improve the efficiency of the local search, different types of parallel local search are distinguished. Codognet et al (2018) present a survey of parallel local searches. It considers different types of local search algorithms (simulated annealing, tabu search and others) and presents existing approaches to parallelizing them.…”

Section: Parallel Local Searchmentioning

confidence: 99%

Widening: using parallel resources to improve model quality

Berthold

Fillbrunn

Siebes

2021

Data Min Knowl Disc

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This paper provides a unified description of Widening, a framework for the use of parallel (or otherwise abundant) computational resources to improve model quality. We discuss different theoretical approaches to Widening with and without consideration of diversity. We then soften some of the underlying constraints so that Widening can be implemented in real world algorithms. We summarize earlier experimental results demonstrating the potential impact as well as promising implementation strategies before concluding with a survey of related work.

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“…Thus, this method can have very poor performance if the calculation cost of evaluations during the exploration is not high enough. However, single-walk methods have proved to be effective in solving different optimisation problems on both multi-thread [8] and GPU architectures [22]. Indeed, as the evaluation is the same algorithm but with different data (the solutions) and without dependencies, GPUs are typically dedicated to single-walk methods.…”

Section: Parallel Multi-objective Local Search (Pmols)mentioning

confidence: 99%

“…Multi-walk methods can be divided into two categories: independent and cooperative approaches. This last category adds a communication mechanism allowing to exchange information in order to improve either the diversification or the intensification between search processes [8].…”

Section: Parallel Multi-objective Local Search (Pmols)mentioning

confidence: 99%