Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/60114
DC FieldValueLanguage
dc.contributor.authorZamuda, Alesen_US
dc.contributor.authorHernández Sosa, José Danielen_US
dc.date.accessioned2020-01-14T10:42:50Z-
dc.date.available2020-01-14T10:42:50Z-
dc.date.issued2019en_US
dc.identifier.issn0957-4174en_US
dc.identifier.otherWoS-
dc.identifier.urihttp://hdl.handle.net/10553/60114-
dc.description.abstractThis paper presents an application of a recently well performing evolutionary algorithm for continuous numerical optimization, Success-History Based Adaptive Differential Evolution Algorithm (SHADE) including Linear population size reduction (L-SHADE), to an expert system for underwater glider path planning (UGPP). The proposed algorithm is compared to other similar algorithms and also to results from literature. The motivation of this work is to provide an alternative to the current glider mission control systems, that are based mostly on multidisciplinary human-expert teams from robotic and oceanographic areas. Initially configured as a decision-support expert system, the natural evolution of the tool is targeting higher autonomy levels.en_US
dc.languageengen_US
dc.relationCost Action Chipseten_US
dc.relation.ispartofExpert Systems with Applicationsen_US
dc.sourceExpert Systems with Applications [ISSN 0957-4174], v. 119, p. 155-170, (Abril 2019)en_US
dc.subject120304 Inteligencia artificialen_US
dc.subject.otherDifferential evolutionen_US
dc.subject.otherLinear population size reductionen_US
dc.subject.otherSuccess-history based parameter adaptationen_US
dc.subject.otherL-SHADEen_US
dc.subject.otherUnderwater glider path planningen_US
dc.titleSuccess history applied to expert system for underwater glider path planning using differential evolutionen_US
dc.typeinfo:eu-repo/semantics/Articleen_US
dc.typeArticleen_US
dc.identifier.doi10.1016/j.eswa.2018.10.048en_US
dc.identifier.scopus85055870442-
dc.identifier.isi000456222700012-
dc.contributor.authorscopusid23975217400-
dc.contributor.authorscopusid57188865208-
dc.identifier.eissn1873-6793-
dc.description.lastpage170en_US
dc.description.firstpage155en_US
dc.relation.volume119en_US
dc.investigacionIngeniería y Arquitecturaen_US
dc.type2Artículoen_US
dc.contributor.daisngid1202017-
dc.contributor.daisngid8050203-
dc.utils.revisionen_US
dc.contributor.wosstandardWOS:Zamuda, A-
dc.contributor.wosstandardWOS:Sosa, JDH-
dc.date.coverdateAbril 2019en_US
dc.identifier.ulpgcen_US
dc.contributor.buulpgcBU-INGen_US
dc.description.sjr1,494
dc.description.jcr5,452
dc.description.sjrqQ1
dc.description.jcrqQ1
dc.description.scieSCIE
item.grantfulltextopen-
item.fulltextCon texto completo-
crisitem.author.deptGIR SIANI: Inteligencia Artificial, Robótica y Oceanografía Computacional-
crisitem.author.deptIU Sistemas Inteligentes y Aplicaciones Numéricas-
crisitem.author.deptDepartamento de Informática y Sistemas-
crisitem.author.orcid0000-0003-3022-7698-
crisitem.author.parentorgIU Sistemas Inteligentes y Aplicaciones Numéricas-
crisitem.author.fullNameHernández Sosa, José Daniel-
crisitem.project.principalinvestigatorHernández Sosa, José Daniel-
Appears in Collections:Artículos
Adobe PDF (1,67 MB)
Show simple item record

Google ScholarTM

Check

Altmetric


Share



Export metadata



Items in accedaCRIS are protected by copyright, with all rights reserved, unless otherwise indicated.