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dc.contributor.authorLatorre De la Fuente, Antonio
dc.contributor.authorMolina Cabrera, Daniel
dc.contributor.authorOsaba Icedo, Eneko
dc.contributor.authorPoyatos Amador, Javier
dc.contributor.authorDel Ser Lorente, Javier ORCID
dc.contributor.authorHerrera Triguero, Francisco
dc.date.accessioned2021-12-15T09:28:04Z
dc.date.available2021-12-15T09:28:04Z
dc.date.issued2021-12
dc.identifier.citationSwarm and Evolutionary Computation 67 : (2021) // Article ID 100973es_ES
dc.identifier.issn2210-6502
dc.identifier.issn2210-6510
dc.identifier.urihttp://hdl.handle.net/10810/54488
dc.description.abstract[EN]Bio-inspired optimization (including Evolutionary Computation and Swarm Intelligence) is a growing research topic with many competitive bio-inspired algorithms being proposed every year. In such an active area, preparing a successful proposal of a new bio-inspired algorithm is not an easy task. Given the maturity of this research field, proposing a new optimization technique with innovative elements is no longer enough. Apart from the novelty, results reported by the authors should be proven to achieve a significant advance over previous outcomes from the state of the art. Unfortunately, not all new proposals deal with this requirement properly. Some of them fail to select appropriate benchmarks or reference algorithms to compare with. In other cases, the validation process carried out is not defined in a principled way (or is even not done at all). Consequently, the significance of the results presented in such studies cannot be guaranteed. In this work we review several recommendations in the literature and propose methodological guidelines to prepare a successful proposal, taking all these issues into account. We expect these guidelines to be useful not only for authors, but also for reviewers and editors along their assessment of new contributions to the field.es_ES
dc.description.sponsorshipThis work was supported by grants from the Spanish Ministry of Science (TIN2016-8113-R, TIN2017-89517-P and TIN2017-83132-C2-2-R) and Universidad Politecnica de Madrid (PINV-18-XEOGHQ-19-4QTEBP) . Eneko Osaba and Javier Del Ser-would also like to thank the Basque Government for its funding support through the ELKARTEK and EMAITEK programs. Javier Del Ser-receives funding support from the Consolidated Research Group MATHMODE (IT1294-19) granted by the Department of Education of the Basque Government.es_ES
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subjectbio-inspired optimizationes_ES
dc.subjectbenchmarkinges_ES
dc.subjectparameter tuninges_ES
dc.subjectcomparison methodologieses_ES
dc.subjectstatistical analysises_ES
dc.subjectrecommendations reviewes_ES
dc.subjectguidelineses_ES
dc.titleA prescription of methodological guidelines for comparing bio-inspired optimization algorithmses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.holder©2021 The Authors. This is an open access article under the CC BY-NC-ND licensees_ES
dc.rights.holderAtribución-NoComercial-SinDerivadas 3.0 España*
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S2210650221001358?via%3Dihubes_ES
dc.identifier.doi10.1016/j.swevo.2021.100973
dc.departamentoesIngeniería de comunicacioneses_ES
dc.departamentoeuKomunikazioen ingeniaritzaes_ES


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©2021 The Authors.  This is an open access article under the CC BY-NC-ND license
Except where otherwise noted, this item's license is described as ©2021 The Authors. This is an open access article under the CC BY-NC-ND license