On the Transferability of Knowledge among Vehicle Routing Problems by using Cellular Evolutionary Multitasking
dc.contributor.author | Osaba, Eneko | |
dc.contributor.author | Martinez, Aritz D. | |
dc.contributor.author | Lobo, Jesus L. | |
dc.contributor.author | Laña, Ibai | |
dc.contributor.author | Ser, Javier Del | |
dc.contributor.institution | Quantum | |
dc.contributor.institution | IA | |
dc.date.issued | 2020-09-20 | |
dc.description | Publisher Copyright: © 2020 IEEE. | |
dc.description.abstract | Multitasking optimization is a recently introduced paradigm, focused on the simultaneous solving of multiple optimization problem instances (tasks). The goal of multitasking environments is to dynamically exploit existing complementarities and synergies among tasks, helping each other through the transfer of genetic material. More concretely, Evolutionary Multitasking (EM) regards to the resolution of multitasking scenarios using concepts inherited from Evolutionary Computation. EM approaches such as the well-known Multifactorial Evolutionary Algorithm (MFEA) are lately gaining a notable research momentum when facing with multiple optimization problems. This work is focused on the application of the recently proposed Multifactorial Cellular Genetic Algorithm (MFCGA) to the well-known Capacitated Vehicle Routing Problem (CVRP). In overall, 11 different multitasking setups have been built using 12 datasets. The contribution of this research is twofold. On the one hand, it is the first application of the MFCGA to the Vehicle Routing Problem family of problems. On the other hand, equally interesting is the second contribution, which is focused on the quantitative analysis of the positive genetic transferability among the problem instances. To do that, we provide an empirical demonstration of the synergies arisen between the different optimization tasks. | en |
dc.description.sponsorship | The authors thank the Basque Government for its support through the Consolidated Research Group MATHMODE (IT1294-19), EMAITEK and ELKARTEK programs, as well as the Spanish Centro para el Desarrollo Tecnologico Industrial (CDTI, Ministry of Science and Innovation) through the “Red Cervera” Programme (AI4ES project). | |
dc.description.status | Peer reviewed | |
dc.identifier.citation | Osaba , E , Martinez , A D , Lobo , J L , Laña , I & Ser , J D 2020 , On the Transferability of Knowledge among Vehicle Routing Problems by using Cellular Evolutionary Multitasking . in 2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020 . , 9294497 , 2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020 , Institute of Electrical and Electronics Engineers Inc. , 23rd IEEE International Conference on Intelligent Transportation Systems, ITSC 2020 , Rhodes , Greece , 20/09/20 . https://doi.org/10.1109/ITSC45102.2020.9294497 | |
dc.identifier.citation | conference | |
dc.identifier.doi | 10.1109/ITSC45102.2020.9294497 | |
dc.identifier.isbn | 9781728141497 | |
dc.identifier.url | http://www.scopus.com/inward/record.url?scp=85099643226&partnerID=8YFLogxK | |
dc.language.iso | eng | |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | |
dc.relation.ispartof | 2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020 | |
dc.relation.ispartofseries | 2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020 | |
dc.relation.projectID | Centro para el Desarrollo Tecnológico Industrial, CDTI | |
dc.relation.projectID | Eusko Jaurlaritza, IT1294-19 | |
dc.relation.projectID | Ministerio de Ciencia e Innovación, MICINN | |
dc.rights | info:eu-repo/semantics/openAccess | |
dc.subject.keywords | Artificial Intelligence | |
dc.subject.keywords | Decision Sciences (miscellaneous) | |
dc.subject.keywords | Information Systems and Management | |
dc.subject.keywords | Modeling and Simulation | |
dc.subject.keywords | Education | |
dc.title | On the Transferability of Knowledge among Vehicle Routing Problems by using Cellular Evolutionary Multitasking | en |
dc.type | conference output |