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A Multi-Adaptive Particle Swarm Optimization for the Vehicle Routing Problem with Time Windows
Technical University of Crete, School of Production Engineering and Management, Chania, Greece.
Technical University of Crete, School of Production Engineering and Management, Chania, Greece.
Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering. Aristotle University of Thessalonike, Department of Civil Engineering, Thessalonike, Greece.ORCID iD: 0000-0001-8473-3663
2019 (English)In: Information Sciences, ISSN 0020-0255, E-ISSN 1872-6291, Vol. 481, p. 311-329Article in journal (Refereed) Published
Abstract [en]

In this paper, a new variant of the Particle Swarm Optimization (PSO) algorithm is proposed for the solution of the Vehicle Routing Problem with Time Windows (VRPTW). Three different adaptive strategies are used in the proposed Multi-Adaptive Particle Swarm Optimization (MAPSO) algorithm. The first adaptive strategy concerns the use of a Greedy Randomized Adaptive Search Procedure (GRASP) that is applied when the initial solutions are produced and when a new solution is created during the iterations of the algorithm. The second adaptive strategy concerns the adaptiveness in the movement of the particles from one solution to another where a new adaptive strategy, the Adaptive Combinatorial Neighborhood Topology, is used. Finally, there is an adaptiveness in all parameters of the Particle Swarm Optimization algorithm. The algorithm starts with random values of the parameters and based on some conditions all parameters are adapted during the iterations. The algorithm was tested in the two classic sets of benchmark instances, the one that includes 56 instances with 100 nodes and the other that includes 300 instances with number of nodes varying between 200 and 1000. The algorithm was compared with other versions of PSO and with the best performing algorithms from the literature.

Place, publisher, year, edition, pages
Elsevier, 2019. Vol. 481, p. 311-329
Keywords [en]
Particle swarm optimization, Vehicle routing problem with time windows, Combinatorial neighborhood topology, Greedy randomized adaptive search procedure, Adaptive strategy
National Category
Reliability and Maintenance
Research subject
Quality Technology & Management
Identifiers
URN: urn:nbn:se:ltu:diva-72530DOI: 10.1016/j.ins.2018.12.086ISI: 000459846300020Scopus ID: 2-s2.0-85059469815OAI: oai:DiVA.org:ltu-72530DiVA, id: diva2:1278227
Note

Validerad;2019;Nivå 2;2019-01-14 (svasva)

Available from: 2019-01-14 Created: 2019-01-14 Last updated: 2019-04-12Bibliographically approved

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Migdalas, Athanasios

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