Please use this identifier to cite or link to this item: http://hdl.handle.net/10174/22719

Title: Parallel Local Search
Authors: Codognet, Philippe
Munera, Danny
Diaz, Daniel
Abreu, Salvador
Editors: Hamadi, Youssef
Sais, Lakhdar
Issue Date: 2018
Publisher: Springer
Abstract: Local search metaheuristics are a recognized means of solving hard com- binatorial problems. Over the last couple of decades, significant advances have been made in terms of the formalization, applicability and performance of these methods. Key to the performance aspect is the increased availability of parallel hardware, which turns out to be largely exploitable by this class of procedures. As real-life cases of combinatorial optimization easily degrade into intractable territory for exact or approximation algorithms, local search metaheuristics hold undeniable interest. This situation is further compounded by the good adequacy exhibited by this class of search procedures for large-scale parallel operation. In this chapter we explore and discuss ways which lead to parallelization in local search.
URI: http://www.springer.com/gp/book/9783319635156
http://hdl.handle.net/10174/22719
Type: bookPart
Appears in Collections:INF - Publicações - Capítulos de Livros

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