Logo do repositório
 
Publicação

Efficient fractional-order modified Harris hawks optimizer for proton exchange membrane fuel cell modeling

2021Artigo científico Acesso embargado
Ver registo simples
dc.contributor.authorYousri, Dalia
dc.contributor.authorMirjalili, Seyedali
dc.contributor.authorTenreiro Machado, J. A.
dc.contributor.authorThanikanti, Sudhakar Babu
dc.contributor.authorelbaksawi, Osama
dc.contributor.authorFathy, Ahmed
dc.date.accessioned2021-09-29T09:15:57Z
dc.date.embargo2031-12
dc.date.issued2021
dc.description.abstractAn effective harmony between the exploration and exploitation phases in meta-heuristics is an essential design consideration to provide reliable performance on a wide range of optimization problems. This paper proposes a novel approach to enhance the exploratory behavior of the Harris hawks optimizer (HHO) based on the fractional calculus (FOC) memory concept. In the proposed variant of the HHO, a hawk moves with a fractional-order velocity, and the rabbit escaping energy is adaptively tuned based on FOC parameters to avoid premature convergence. As a result, the fractional-order modified Harris hawks optimizer (FMHHO) is proposed. The sensitivity of the algorithm performance vis-a-vis the FOC parameters is addressed, and the best variant is recommended based on twenty-three benchmarks. For validating the quality of the proposed variant, twenty-eight benchmarks of CEC2017 are tested. For evaluating the proposed variant against the other present-day techniques, several statistical measures and non-parametric tests are performed. Moreover, to demonstrate the applicability of the proposed technique, the proton exchange membrane fuel cell (PEMFC) model parameters estimation process is handled based on several measured datasets. In this series of experiments, the FMHHO variant is compared with the standard HHO and the other techniques based on intensive statistical metrics, mean convergence curves, and dataset fitting. The overall outcome shows that the FOC memory property improves the performance of the classical HHO and leads to accurate and robust solutions fitting the measured data.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1016/j.engappai.2021.104193pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.22/18611
dc.language.isoengpt_PT
dc.publisherElsevierpt_PT
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S0952197621000403pt_PT
dc.subjectFractional-order modified HHOpt_PT
dc.subjectWhale Optimization Algorithmpt_PT
dc.subjectParticle Swarm Optimizationpt_PT
dc.subjectHarris hawks optimizationpt_PT
dc.subjectSalp Swarm Algorithmpt_PT
dc.subjectParameters estimationpt_PT
dc.subjectGrey Wolf Optimizerpt_PT
dc.subjectFractional calculuspt_PT
dc.subjectGenetic Algorithmpt_PT
dc.subjectOptimizationpt_PT
dc.subjectFuel cellpt_PT
dc.titleEfficient fractional-order modified Harris hawks optimizer for proton exchange membrane fuel cell modelingpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.startPage104193pt_PT
oaire.citation.titleEngineering Applications of Artificial Intelligencept_PT
oaire.citation.volume100pt_PT
person.familyNameTenreiro Machado
person.givenNameJ. A.
person.identifier.ciencia-id7A18-4935-5B29
person.identifier.orcid0000-0003-4274-4879
person.identifier.ridM-2173-2013
person.identifier.scopus-author-id55989030100
rcaap.rightsembargoedAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication82cd5c17-07b6-492b-b3e3-ecebdad1254f
relation.isAuthorOfPublication.latestForDiscovery82cd5c17-07b6-492b-b3e3-ecebdad1254f

Ficheiros

Principais
A mostrar 1 - 1 de 1
Miniatura indisponível
Nome:
ART_DEE_JTM_EAAI_2021.pdf
Tamanho:
3.47 MB
Formato:
Adobe Portable Document Format
Ver/Abrir

Coleções

ISEP - DEE - Artigos