<efrbr:recordSet xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:efrbr="http://vfrbr.info/efrbr/1.1" xmlns:efrbr-work="http://vfrbr.info/efrbr/1.1/work" xmlns:efrbr-expression="http://vfrbr.info/efrbr/1.1/expression" xmlns:efrbr-manifestation="http://vfrbr.info/efrbr/1.1/manifestation" xmlns:efrbr-person="http://vfrbr.info/efrbr/1.1/person" xmlns:efrbr-corporateBody="http://vfrbr.info/efrbr/1.1/corporateBody" xmlns:efrbr-concept="http://vfrbr.info/efrbr/1.1/concept" xmlns:efrbr-structure="http://vfrbr.info/efrbr/1.1/structure" xmlns:efrbr-responsible="http://vfrbr.info/efrbr/1.1/responsible" xmlns:efrbr-subject="http://vfrbr.info/efrbr/1.1/subject" xmlns:efrbr-other="http://vfrbr.info/efrbr/1.1/other" xsi:schemaLocation="http://vfrbr.info/efrbr/1.1 http://vfrbr.info/schemas/1.1/efrbr.xsd"><efrbr:entities><efrbr-work:work identifier="http://purl.tuc.gr/dl/dias/FB185C51-19BE-4ED8-B426-1532957ADA4D"><efrbr-work:titleOfTheWork>A comparative analysis of three computational-intelligence metaheuristic methods for the optimization of TDEM data</efrbr-work:titleOfTheWork></efrbr-work:work><efrbr-expression:expression identifier="http://purl.tuc.gr/dl/dias/FB185C51-19BE-4ED8-B426-1532957ADA4D"><efrbr-expression:titleOfTheExpression>A comparative analysis of three computational-intelligence metaheuristic methods for the optimization of TDEM data</efrbr-expression:titleOfTheExpression><efrbr-expression:formOfExpression vocabulary="DIAS:TYPES">
            Peer-Reviewed Journal Publication
            Δημοσίευση σε Περιοδικό με Κριτές
         </efrbr-expression:formOfExpression><efrbr-expression:dateOfExpression type="issued">2024-02-09</efrbr-expression:dateOfExpression><efrbr-expression:dateOfExpression type="published">2022</efrbr-expression:dateOfExpression><efrbr-expression:languageOfExpression vocabulary="iso639-1">en</efrbr-expression:languageOfExpression><efrbr-expression:summarizationOfContent>We focus on the performances of three nature-inspired metaheuristic methods for the optimization of time-domain electromagnetic (TDEM) data: the Genetic Algorithm (GA), the Particle Swarm Optimization (PSO) and the Grey Wolf Optimizer (GWO) algorithms. While GA and PSO have been used in a plethora of geophysical applications, GWO has received little attention in the literature so far, despite promising outcomes. This study directly and quantitatively compares GA, PSO and GWO applied to TDEM data. To date, these three algorithms have only been compared in pairs. The methods were first applied to a synthetic example of noise-corrupted data and then to two field surveys carried out in Italy. Real data from the first survey refer to a TDEM sounding acquired for groundwater prospection over a known stratigraphy. The data set from the second survey deals with the characterization of a geothermal reservoir. The resulting resistivity models are quantitatively compared to provide a thorough overview of the performances of the algorithms. The comparative analysis reveals that PSO and GWO perform better than GA. GA yields the highest data misfit and an ineffective minimization of the objective function. PSO and GWO provide similar outcomes in terms of both resistivity distribution and data misfits, thus providing compelling evidence that both the emerging GWO and the established PSO are highly valid tools for stochastic inverse modeling in geophysics.</efrbr-expression:summarizationOfContent><efrbr-expression:useRestrictionsOnTheExpression type="creative-commons">http://creativecommons.org/licenses/by/4.0/</efrbr-expression:useRestrictionsOnTheExpression><efrbr-expression:note type="journal name">Pure and Applied Geophysics</efrbr-expression:note><efrbr-expression:note type="journal volume">179</efrbr-expression:note><efrbr-expression:note type="journal number">10</efrbr-expression:note><efrbr-expression:note type="page range">3727–3749</efrbr-expression:note></efrbr-expression:expression><efrbr-manifestation:manifestation identifier="https://dias.library.tuc.gr/view/98662"><efrbr-manifestation:titleOfTheManifestation>Pace_et_al_Pure Appl. Geophys._179(10)_2022.pdf</efrbr-manifestation:titleOfTheManifestation><efrbr-manifestation:publicationDistribution><efrbr-manifestation:placeOfPublicationDistribution type="distribution">Chania [Greece]</efrbr-manifestation:placeOfPublicationDistribution><efrbr-manifestation:publisherDistributor type="distributor">Library of TUC</efrbr-manifestation:publisherDistributor><efrbr-manifestation:dateOfPublicationDistribution>2024-02-09</efrbr-manifestation:dateOfPublicationDistribution></efrbr-manifestation:publicationDistribution><efrbr-manifestation:formOfCarrier>application/pdf</efrbr-manifestation:formOfCarrier><efrbr-manifestation:extentOfTheCarrier>5.9 MB</efrbr-manifestation:extentOfTheCarrier><efrbr-manifestation:accessRestrictionsOnTheManifestation>free</efrbr-manifestation:accessRestrictionsOnTheManifestation></efrbr-manifestation:manifestation><efrbr-person:person identifier="B7A914F1-20FC-4037-B79E-8B7921F81A7F"><efrbr-person:nameOfPerson vocabulary="">
            Pace Francesca
         </efrbr-person:nameOfPerson></efrbr-person:person><efrbr-person:person identifier="http://users.isc.tuc.gr/~araftogianni"><efrbr-person:nameOfPerson vocabulary="TUC:LDAP">
            Raftogianni Adamantia
            Ραυτογιαννη Αδαμαντια
         </efrbr-person:nameOfPerson></efrbr-person:person><efrbr-person:person identifier="9C223E48-3EC0-4E32-BE23-023E494BCD92"><efrbr-person:nameOfPerson vocabulary="">
            Godio Alberto
         </efrbr-person:nameOfPerson></efrbr-person:person><efrbr-corporateBody:corporateBody identifier="https://v2.sherpa.ac.uk/id/publisher/3291"><efrbr-corporateBody:nameOfTheCorporateBody vocabulary="S/R:PUBLISHERS">
            Springer
         </efrbr-corporateBody:nameOfTheCorporateBody></efrbr-corporateBody:corporateBody><efrbr-concept:concept identifier="AD6FB015-4EA1-4F72-97C8-773F62A6E8F1"><efrbr-concept:termForTheConcept>
            Stochastic inverse modeling
         </efrbr-concept:termForTheConcept></efrbr-concept:concept><efrbr-concept:concept identifier="8F9A01E5-ABB9-46F1-B802-ED79477176C2"><efrbr-concept:termForTheConcept>
            Time-domain electromagnetic data
         </efrbr-concept:termForTheConcept></efrbr-concept:concept><efrbr-concept:concept identifier="CAE38C99-3C49-4A35-8475-40E199DA954D"><efrbr-concept:termForTheConcept>
            Particle swarm optimization
         </efrbr-concept:termForTheConcept></efrbr-concept:concept><efrbr-concept:concept identifier="0B54EB4A-93F3-4282-B3DF-5B33A8C2F825"><efrbr-concept:termForTheConcept>
            Genetic algorithm
         </efrbr-concept:termForTheConcept></efrbr-concept:concept><efrbr-concept:concept identifier="D15DAFE4-E3B3-4019-9644-DB59955472F0"><efrbr-concept:termForTheConcept>
            Grey wolf optimizer
         </efrbr-concept:termForTheConcept></efrbr-concept:concept><efrbr-concept:concept identifier="D32C52DB-5C44-4801-97F9-70C858EA1BD8"><efrbr-concept:termForTheConcept>
            Computational swarm intelligence
         </efrbr-concept:termForTheConcept></efrbr-concept:concept></efrbr:entities><efrbr:relationships><efrbr-structure:structureRelations><efrbr-structure:realizedThrough sourceEntity="work" sourceURI="http://purl.tuc.gr/dl/dias/FB185C51-19BE-4ED8-B426-1532957ADA4D" targetEntity="expression" targetURI="http://purl.tuc.gr/dl/dias/FB185C51-19BE-4ED8-B426-1532957ADA4D"/><efrbr-structure:embodiedIn sourceEntity="expression" sourceURI="http://purl.tuc.gr/dl/dias/FB185C51-19BE-4ED8-B426-1532957ADA4D" targetEntity="manifestation" targetURI="http://purl.tuc.gr/dl/dias/EC4B1108-E9BE-4856-96C7-2645C5C305E0"/></efrbr-structure:structureRelations><efrbr-responsible:responsibleRelations><efrbr-responsible:createdBy sourceEntity="work" sourceURI="http://purl.tuc.gr/dl/dias/FB185C51-19BE-4ED8-B426-1532957ADA4D" targetEntity="person" targetURI="B7A914F1-20FC-4037-B79E-8B7921F81A7F"/><efrbr-responsible:realizedBy sourceEntity="expression" sourceURI="http://purl.tuc.gr/dl/dias/FB185C51-19BE-4ED8-B426-1532957ADA4D" targetEntity="person" targetURI="B7A914F1-20FC-4037-B79E-8B7921F81A7F" role="author"/><efrbr-responsible:realizedBy sourceEntity="expression" sourceURI="http://purl.tuc.gr/dl/dias/FB185C51-19BE-4ED8-B426-1532957ADA4D" targetEntity="person" targetURI="http://users.isc.tuc.gr/~araftogianni" role="author"/><efrbr-responsible:realizedBy sourceEntity="expression" sourceURI="http://purl.tuc.gr/dl/dias/FB185C51-19BE-4ED8-B426-1532957ADA4D" targetEntity="person" targetURI="9C223E48-3EC0-4E32-BE23-023E494BCD92" role="author"/><efrbr-responsible:realizedBy sourceEntity="expression" sourceURI="http://purl.tuc.gr/dl/dias/FB185C51-19BE-4ED8-B426-1532957ADA4D" targetEntity="person" targetURI="https://v2.sherpa.ac.uk/id/publisher/3291" role="publisher"/></efrbr-responsible:responsibleRelations><efrbr-subject:subjectRelations><efrbr-subject:hasSubject sourceEntity="work" sourceURI="http://purl.tuc.gr/dl/dias/FB185C51-19BE-4ED8-B426-1532957ADA4D" targetEntity="concept" targetURI="AD6FB015-4EA1-4F72-97C8-773F62A6E8F1"/><efrbr-subject:hasSubject sourceEntity="work" sourceURI="http://purl.tuc.gr/dl/dias/FB185C51-19BE-4ED8-B426-1532957ADA4D" targetEntity="concept" targetURI="8F9A01E5-ABB9-46F1-B802-ED79477176C2"/><efrbr-subject:hasSubject sourceEntity="work" sourceURI="http://purl.tuc.gr/dl/dias/FB185C51-19BE-4ED8-B426-1532957ADA4D" targetEntity="concept" targetURI="CAE38C99-3C49-4A35-8475-40E199DA954D"/><efrbr-subject:hasSubject sourceEntity="work" sourceURI="http://purl.tuc.gr/dl/dias/FB185C51-19BE-4ED8-B426-1532957ADA4D" targetEntity="concept" targetURI="0B54EB4A-93F3-4282-B3DF-5B33A8C2F825"/><efrbr-subject:hasSubject sourceEntity="work" sourceURI="http://purl.tuc.gr/dl/dias/FB185C51-19BE-4ED8-B426-1532957ADA4D" targetEntity="concept" targetURI="D15DAFE4-E3B3-4019-9644-DB59955472F0"/><efrbr-subject:hasSubject sourceEntity="work" sourceURI="http://purl.tuc.gr/dl/dias/FB185C51-19BE-4ED8-B426-1532957ADA4D" targetEntity="concept" targetURI="D32C52DB-5C44-4801-97F9-70C858EA1BD8"/></efrbr-subject:subjectRelations><efrbr-other:otherRelations/></efrbr:relationships></efrbr:recordSet>