000108338 001__ 108338
000108338 005__ 20230519145404.0
000108338 0247_ $$2doi$$a10.1016/j.neucom.2019.11.122
000108338 0248_ $$2sideral$$a122542
000108338 037__ $$aART-2021-122542
000108338 041__ $$aeng
000108338 100__ $$aJordán, J.
000108338 245__ $$aLocalization of charging stations for electric vehicles using genetic algorithms
000108338 260__ $$c2021
000108338 5060_ $$aAccess copy available to the general public$$fUnrestricted
000108338 5203_ $$aThe electric vehicle (EV) is gradually being introduced in cities. The impact of this introduction is less due, among other reasons, to the lack of charging infrastructure necessary to satisfy the demand. In today''s cities there is no adequate infrastructure and it is necessary to have action plans that allow an easy deployment of a network of EV charging points in current cities. These action plans should try to place the EV charging stations in the most appropriate places for optimizing their use. According to this, this paper presents an agent-oriented approach that analyses the different configurations of possible locations of charging stations for the electric vehicles in a specific city. The proposed multi-agent system takes into account data from a variety of sources such as social networks activity and mobility information in order to estimate the best configurations. The proposed approach employs a genetic algorithm (GA) that tries to optimize the possible configurations of the charging infrastructure. Additionally, a new crossover method for the GA is proposed considering this context.
000108338 536__ $$9info:eu-repo/grantAgreement/ES/MINECO-FEDER/RTI2018-095390-B-C31$$9info:eu-repo/grantAgreement/ES/MINECO/MODINVECI
000108338 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
000108338 590__ $$a5.779$$b2021
000108338 591__ $$aCOMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE$$b39 / 146 = 0.267$$c2021$$dQ2$$eT1
000108338 594__ $$a10.3$$b2021
000108338 592__ $$a1.66$$b2021
000108338 593__ $$aCognitive Neuroscience$$c2021$$dQ1
000108338 593__ $$aArtificial Intelligence$$c2021$$dQ1
000108338 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000108338 700__ $$aPalanca, J.
000108338 700__ $$0(orcid)0000-0002-1279-3429$$adel Val, E.$$uUniversidad de Zaragoza
000108338 700__ $$aJulian, V.
000108338 700__ $$aBotti, V.
000108338 7102_ $$15007$$2570$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Lenguajes y Sistemas Inf.
000108338 773__ $$g452 (2021), 416-423$$pNeurocomputing$$tNeurocomputing$$x0925-2312
000108338 8564_ $$s1209161$$uhttps://zaguan.unizar.es/record/108338/files/texto_completo.pdf$$yPostprint
000108338 8564_ $$s2698459$$uhttps://zaguan.unizar.es/record/108338/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint
000108338 909CO $$ooai:zaguan.unizar.es:108338$$particulos$$pdriver
000108338 951__ $$a2023-05-18-13:45:03
000108338 980__ $$aARTICLE