Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration Versus Algorithmic Behavior, Critical Analysis Recommendations

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dc.contributor.authorMolina, Daniel
dc.contributor.authorPoyatos, Javier
dc.contributor.authorSer, Javier Del
dc.contributor.authorGarcía, Salvador
dc.contributor.authorHussain, Amir
dc.contributor.authorHerrera, Francisco
dc.contributor.institutionIA
dc.date.issued2020-09-01
dc.descriptionPublisher Copyright: © 2020, Springer Science+Business Media, LLC, part of Springer Nature.
dc.description.abstractIn recent algorithmic family simulates different biological processes observed in Nature in order to efficiently address complex optimization problems. In the last years the number of bio-inspired optimization approaches in literature has grown considerably, reaching unprecedented levels that dark the future prospects of this field of research. This paper addresses this problem by proposing two comprehensive, principle-based taxonomies that allow researchers to organize existing and future algorithmic developments into well-defined categories, considering two different criteria: the source of inspiration and the behavior of each algorithm. Using these taxonomies we review more than three hundred publications dealing with nature-inspired and bio-inspired algorithms, and proposals falling within each of these categories are examined, leading to a critical summary of design trends and similarities between them, and the identification of the most similar classical algorithm for each reviewed paper. From our analysis we conclude that a poor relationship is often found between the natural inspiration of an algorithm and its behavior. Furthermore, similarities in terms of behavior between different algorithms are greater than what is claimed in their public disclosure: specifically, we show that more than one-third of the reviewed bio-inspired solvers are versions of classical algorithms. Grounded on the conclusions of our critical analysis, we give several recommendations and points of improvement for better methodological practices in this active and growing research field.en
dc.description.sponsorshipThis work was supported by grants from the Spanish Ministry of Science and the European Fund (FEDER) under projects TIN2016-81113R, and TIN2017-89517-P. Javier Del Ser received support from the Basque Government through the ELKARTEK and EMAITEK funding programs.
dc.description.statusPeer reviewed
dc.format.extent43
dc.identifier.citationMolina , D , Poyatos , J , Ser , J D , García , S , Hussain , A & Herrera , F 2020 , ' Comprehensive Taxonomies of Nature- and Bio-inspired Optimization : Inspiration Versus Algorithmic Behavior, Critical Analysis Recommendations ' , Cognitive Computation , vol. 12 , no. 5 , pp. 897-939 . https://doi.org/10.1007/s12559-020-09730-8
dc.identifier.doi10.1007/s12559-020-09730-8
dc.identifier.issn1866-9956
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85084340371&partnerID=8YFLogxK
dc.language.isoeng
dc.relation.ispartofCognitive Computation
dc.relation.projectIDEuropean Fund
dc.relation.projectIDEusko Jaurlaritza
dc.relation.projectIDMinisterio de Ciencia e Innovación, MICINN
dc.relation.projectIDEuropean Regional Development Fund, FEDER, TIN2016-81113R-TIN2017-89517-P
dc.relation.projectIDMinisterio de Economía, Industria y Competitividad, Gobierno de España, MINECO
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subject.keywordsBio-inspired optimization
dc.subject.keywordsClassification
dc.subject.keywordsNature-inspired algorithms
dc.subject.keywordsTaxonomy
dc.subject.keywordsComputer Vision and Pattern Recognition
dc.subject.keywordsComputer Science Applications
dc.subject.keywordsCognitive Neuroscience
dc.titleComprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration Versus Algorithmic Behavior, Critical Analysis Recommendationsen
dc.typejournal article
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