A prescription of methodological guidelines for comparing bio-inspired optimization algorithms

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dc.contributor.authorLaTorre, Antonio
dc.contributor.authorMolina, Daniel
dc.contributor.authorOsaba, Eneko
dc.contributor.authorPoyatos, Javier
dc.contributor.authorDel Ser, Javier
dc.contributor.authorHerrera, Francisco
dc.contributor.institutionQuantum
dc.contributor.institutionIA
dc.date.issued2021-12
dc.descriptionPublisher Copyright: © 2021
dc.description.abstractBio-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.en
dc.description.statusPeer reviewed
dc.format.extent1
dc.format.extent2884096
dc.identifier.citationLaTorre , A , Molina , D , Osaba , E , Poyatos , J , Del Ser , J & Herrera , F 2021 , ' A prescription of methodological guidelines for comparing bio-inspired optimization algorithms ' , Swarm and Evolutionary Computation , vol. 67 , 100973 , pp. 100973 . https://doi.org/10.1016/j.swevo.2021.100973
dc.identifier.doi10.1016/j.swevo.2021.100973
dc.identifier.issn2210-6502
dc.identifier.otherresearchoutputwizard: 11556/1193
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85114019381&partnerID=8YFLogxK
dc.language.isoeng
dc.relation.ispartofSwarm and Evolutionary Computation
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subject.keywordsBio-inspired optimization
dc.subject.keywordsBenchmarking
dc.subject.keywordsParameter tuning
dc.subject.keywordsComparison methodologies
dc.subject.keywordsStatistical analysis
dc.subject.keywordsRecommendations review
dc.subject.keywordsGuidelines
dc.subject.keywordsBio-inspired optimization
dc.subject.keywordsBenchmarking
dc.subject.keywordsParameter tuning
dc.subject.keywordsComparison methodologies
dc.subject.keywordsStatistical analysis
dc.subject.keywordsRecommendations review
dc.subject.keywordsGuidelines
dc.subject.keywordsGeneral Computer Science
dc.subject.keywordsGeneral Mathematics
dc.subject.keywordsFunding Info
dc.subject.keywordsThis 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 Politécnica 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.
dc.subject.keywordsThis 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 Politécnica 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.
dc.titleA prescription of methodological guidelines for comparing bio-inspired optimization algorithmsen
dc.typejournal article
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