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A Clonal Selection Algorithm for Multiobjective Energy Reduction Multi-Depot Vehicle Routing Problem
Vise andre og tillknytning
2019 (engelsk)Inngår i: LOD 2018: Machine Learning, Optimization, and Data Science / [ed] Giuseppe Nicosia, Springer Publishing Company, 2019, s. 381-393Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Clonal Selection Algorithm is a very powerful NatureInspired Algorithm that has been applied in a number of different kindof optimization problems since the time it was first published. Also, inrecent years a growing number of optimization models have been pro-posed that are trying to reduce the energy consumption in vehicle rout-ing. In this paper, a new variant of Clonal Selection Algorithm, theParallel Multi-Start Multiobjective Clonal Selection Algorithm (PMS-MOCSA) is proposed for the solution of a Vehicle Routing Problem vari-ant, the Multiobjective Energy Reduction Multi-Depot Vehicle RoutingProblem (MERMDVRP). In the formulation four different scenarios areproposed where the distances between the customers and the depots areeither symmetric or asymmetric and the customers have either demandor pickup. The algorithm is compared with two other multiobjective algo-rithms, the Parallel Multi-Start Non-dominated Sorting Differential Evo-lution (PMS-NSDE) and the Parallel Multi-Start Non-dominated Sort-ing Genetic Algorithm II (PMS-NSGA II) for a number of benchmarkinstances.

sted, utgiver, år, opplag, sider
Springer Publishing Company, 2019. s. 381-393
Serie
Lecture Notes in Computer Science, ISSN 1611-3349 ; 11331
Emneord [en]
Vehicle Routing Problem, Clonal Selection Algorithm, NSGA II, NSDE, VNS
HSV kategori
Identifikatorer
URN: urn:nbn:se:ltu:diva-73561DOI: 10.1007/978-3-030-13709-0ISBN: 978-3-030-13708-3 (tryckt)OAI: oai:DiVA.org:ltu-73561DiVA, id: diva2:1303763
Konferanse
Machine Learning, Optimization, and Data Science 4th International Conference, LOD 2018, Volterra, Italy, September 13-16, 2018
Tilgjengelig fra: 2019-04-10 Laget: 2019-04-10 Sist oppdatert: 2019-09-25

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