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dc.contributor.authorSinha A.
dc.contributor.authorMalo P.
dc.contributor.authorDeb K.
dc.date.accessioned2022-02-11T10:16:29Z
dc.date.available2022-02-11T10:16:29Z
dc.date.issued2018
dc.identifier.citationSinha, A., Malo, P., & Deb, K. (2018). A Review on Bilevel Optimization: From Classical to Evolutionary Approaches and Applications. In IEEE Transactions on Evolutionary Computation (Vol. 22, Issue 2). https://doi.org/10.1109/TEVC.2017.2712906
dc.identifier.issn1089778X
dc.identifier.urihttps://www.doi.org/10.1109/TEVC.2017.2712906
dc.identifier.urihttp://hdl.handle.net/11718/25384
dc.description.abstractBilevel optimization is defined as a mathematical program, where an optimization problem contains another optimization problem as a constraint. These problems have received significant attention from the mathematical programming community. Only limited work exists on bilevel problems using evolutionary computation techniques; however, recently there has been an increasing interest due to the proliferation of practical applications and the potential of evolutionary algorithms in tackling these problems. This paper provides a comprehensive review on bilevel optimization from the basic principles to solution strategies; both classical and evolutionary. A number of potential application problems are also discussed. To offer the readers insights on the prominent developments in the field of bilevel optimization, we have performed an automated text-analysis of an extended list of papers published on bilevel optimization to date. This paper should motivate evolutionary computation researchers to pay more attention to this practical yet challenging area. � 2017 IEEE.
dc.language.isoen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartofIEEE Transactions on Evolutionary Computation
dc.subjectBilevel optimization
dc.subjectevolutionary algorithms
dc.subjectStackelberg games
dc.titleA Review on Bilevel Optimization: From Classical to Evolutionary Approaches and Applications
dc.typeReview
dc.rights.licenseCC BY
dc.contributor.affiliationDepartment of Production and Quantitative Methods, Indian Institute of Management Ahmedabad, Ahmedabad, 380015, India
dc.contributor.affiliationDepartment of Information and Service Economy, Aalto University School of Business, Aalto, 00076, Finland
dc.contributor.affiliationDepartment of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824, United States
dc.contributor.institutionauthorSinha, A., Department of Production and Quantitative Methods, Indian Institute of Management Ahmedabad, Ahmedabad, 380015, India
dc.contributor.institutionauthorMalo, P., Department of Information and Service Economy, Aalto University School of Business, Aalto, 00076, Finland
dc.contributor.institutionauthorDeb, K., Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824, United States
dc.description.scopusid56443280300
dc.description.scopusid18037486300
dc.description.scopusid7006019904
dc.identifier.doi10.1109/TEVC.2017.2712906
dc.identifier.endpage295
dc.identifier.startpage276
dc.identifier.issue2
dc.identifier.volume22


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