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dc.contributor.authorMisitano, Giovanni
dc.contributor.authorSaini, Bhupinder Singh
dc.contributor.authorAfsar, Bekir
dc.contributor.authorShavazipour, Babooshka
dc.contributor.authorMiettinen Kaisa
dc.date.accessioned2021-11-15T12:37:50Z
dc.date.available2021-11-15T12:37:50Z
dc.date.issued2021
dc.identifier.citationMisitano, Giovanni, Saini, Bhupinder Singh, Afsar, Bekir, Shavazipour, Babooshka, Miettinen Kaisa. (2021). DESDEO: The Modular and Open Source Framework for Interactive Multiobjective Optimization. <i>IEEE Access</i>, <i>9</i>, 148277-148295. <a href="https://doi.org/10.1109/ACCESS.2021.3123825" target="_blank">https://doi.org/10.1109/ACCESS.2021.3123825</a>
dc.identifier.otherCONVID_101891952
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/78657
dc.description.abstractInteractive multiobjective optimization methods incorporate preferences from a human decision maker in the optimization process iteratively. This allows the decision maker to focus on a subset of solutions, learn about the underlying trade-offs among the conflicting objective functions in the problem and adjust preferences during the solution process. Incorporating preference information allows computing only solutions that are interesting to the decision maker, decreasing computation time significantly. Thus, interactive methods have many strengths making them viable for various applications. However, there is a lack of existing software frameworks to apply and experiment with interactive methods. We fill a gap in the optimization software available and introduce DESDEO, a modular and open source Python framework for interactive multiobjective optimization. DESDEO’s modular structure enables implementing new interactive methods and reusing previously implemented ones and their functionalities. Both scalarization-based and evolutionary methods are supported, and DESDEO allows hybridizing interactive methods of both types in novel ways and enables even switching the method during the solution process. Moreover, DESDEO also supports defining multiobjective optimization problems of different kinds, such as data-driven or simulation-based problems. We discuss DESDEO’s modular structure in detail and demonstrate its capabilities in four carefully chosen use cases aimed at helping readers unfamiliar with DESDEO get started using it. We also give an example on how DESDEO can be extended with a graphical user interface. Overall, DESDEO offers a much-needed toolbox for researchers and practitioners to efficiently develop and apply interactive methods in new ways – both in academia and industry.en
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartofseriesIEEE Access
dc.rightsCC BY 4.0
dc.subject.otherdata-driven multiobjective optimization
dc.subject.otherevolutionary computation
dc.subject.otherinteractive methods
dc.subject.othermulti-criteria decision making
dc.subject.othernonlinear optimization
dc.subject.otheropen source software
dc.subject.otherPareto optimization
dc.titleDESDEO: The Modular and Open Source Framework for Interactive Multiobjective Optimization
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-202111155670
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.contributor.oppiaineLaskennallinen tiedefi
dc.contributor.oppiaineMultiobjective Optimization Groupfi
dc.contributor.oppiainePäätöksen teko monitavoitteisestifi
dc.contributor.oppiaineComputational Scienceen
dc.contributor.oppiaineMultiobjective Optimization Groupen
dc.contributor.oppiaineDecision analytics utilizing causal models and multiobjective optimizationen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.description.reviewstatuspeerReviewed
dc.format.pagerange148277-148295
dc.relation.issn2169-3536
dc.relation.volume9
dc.type.versionpublishedVersion
dc.rights.copyright© 2021 the Authors
dc.rights.accesslevelopenAccessfi
dc.relation.grantnumber322221
dc.subject.ysolineaarinen optimointi
dc.subject.ysooptimointi
dc.subject.ysopäätöksenteko
dc.subject.ysopareto-tehokkuus
dc.subject.ysoevoluutiolaskenta
dc.subject.ysomonitavoiteoptimointi
dc.subject.ysoavoin lähdekoodi
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p15483
jyx.subject.urihttp://www.yso.fi/onto/yso/p13477
jyx.subject.urihttp://www.yso.fi/onto/yso/p8743
jyx.subject.urihttp://www.yso.fi/onto/yso/p28039
jyx.subject.urihttp://www.yso.fi/onto/yso/p28071
jyx.subject.urihttp://www.yso.fi/onto/yso/p32016
jyx.subject.urihttp://www.yso.fi/onto/yso/p17089
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.1109/ACCESS.2021.3123825
dc.relation.funderResearch Council of Finlanden
dc.relation.funderSuomen Akatemiafi
jyx.fundingprogramAcademy Project, AoFen
jyx.fundingprogramAkatemiahanke, SAfi
jyx.fundinginformationThis work was supported by the Academy of Finland under Grant 322221.
dc.type.okmA1


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