Please use this identifier to cite or link to this item: https://ruomo.lib.uom.gr/handle/7000/589
Title: On a Cooperative VNS Parallelization Strategy for the Capacitated Vehicle Routing Problem
Authors: Kalatzantonakis, Panagiotis
Sifaleras, Angelo
Samaras, Nikolaos
Editors: Matsatsinis, Nikolaos F
Marinakis, Yannis
Pardalos, Panos
Type: Conference paper
Subjects: FRASCATI::Natural sciences::Mathematics::Applied Mathematics
FRASCATI::Natural sciences::Computer and information sciences
Keywords: Parallel metaheuristics
Variable neighborhood search
Cooperation strategies
Vehicle routing problem
Intelligent optimization methods
Issue Date: 2020
Publisher: Springer
Volume: 11968
First Page: 231
Last Page: 239
Volume Title: Learning and Intelligent Optimization. LION 2019
Part of Series: Lecture Notes in Computer Science
Part of Series: Lecture Notes in Computer Science
Abstract: It is generally accepted that cooperation-based strategies in parallel metaheuristics exhibit better performances in contrast with non-cooperative approaches. In this paper, we study how the cooperation between processes affects the performance and solution quality of parallel algorithms. The purpose of this study is to provide researchers with a practical starting point for designing better cooperation strategies in parallel metaheuristics. To achieve that, we propose two parallel models based on the general variable neighborhood search (GVNS) to solve the capacitated vehicle routing problem (CVRP). Both models scan the search space by using multiple search processes in parallel. The first model lacks communication, while on the other hand, the second model follows a strategy based on information exchange. The received solutions are utilized to guide the search. We conduct an experimental study using well-known benchmark instances of the CVRP, in which the usefulness of communication throughout the search process is assessed. The findings confirm that careful design of the cooperation strategy in parallel metaheuristics can yield better results.
URI: https://doi.org/10.1007/978-3-030-38629-0_19
https://ruomo.lib.uom.gr/handle/7000/589
ISBN: 978-3-030-38628-3
978-3-030-38629-0
ISSN: 0302-9743
1611-3349
Other Identifiers: 10.1007/978-3-030-38629-0_19
Appears in Collections:Department of Applied Informatics

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