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dc.contributor.authorLlano García, J.L.
dc.contributor.authorMonroy, R.
dc.contributor.authorSosa Hernández, V.A.
dc.contributor.authorCoello, C.A. 
dc.date.accessioned2022-01-02T12:25:04Z
dc.date.available2022-01-02T12:25:04Z
dc.date.issued2021-12-01
dc.identifier.issn22106502
dc.identifier.urihttp://hdl.handle.net/20.500.11824/1406
dc.description.abstractMany real-world applications involve dealing with several conflicting objectives which need to be optimized simultaneously. Moreover, these problems may require the consideration of limitations that restrict their decision variable space. Evolutionary Algorithms (EAs) are capable of tackling Multi-objective Optimization Problems (MOPs). However, these approaches struggle to accurately approximate a feasible solution when considering equality constraints as part of the problem due to the inability of EAs to find and keep solutions exactly at the constraint boundaries. Here, we present an indicator-based evolutionary multi-objective optimization algorithm (EMOA) for tackling Equality Constrained MOPs (ECMOPs). In our proposal, we adopt an artificially constructed reference set closely resembling the feasible Pareto front of an ECMOP to calculate the Inverted Generational Distance of a population, which is then used as a density estimator. An empirical study over a set of benchmark problems each of which contains at least one equality constraint was performed to test the capabilities of our proposed COnstrAined Reference SEt - EMOA (COARSE-EMOA). Our results are compared to those obtained by six other EMOAs. As will be shown, our proposed COARSE-EMOA can properly approximate a feasible solution by guiding the search through the use of an artificially constructed set that approximates the feasible Pareto front of a given problem.en_US
dc.formatapplication/pdfen_US
dc.language.isoengen_US
dc.rightsReconocimiento-NoComercial-CompartirIgual 3.0 Españaen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/es/en_US
dc.subjectConstrained optimizationen_US
dc.subjectEvolutionary algorithmsen_US
dc.subjectMulti-Objective optimizationen_US
dc.subjectPerformance indicatorsen_US
dc.titleCOARSE-EMOA: An indicator-based evolutionary algorithm for solving equality constrained multi-objective optimization problemsen_US
dc.typeinfo:eu-repo/semantics/articleen_US
dc.identifier.doi10.1016/j.swevo.2021.100983en_US
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen_US
dc.type.hasVersioninfo:eu-repo/semantics/submittedVersionen_US
dc.journal.titleSwarm and Evolutionary Computationen_US
dc.volume.number67en_US


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Reconocimiento-NoComercial-CompartirIgual 3.0 España
Except where otherwise noted, this item's license is described as Reconocimiento-NoComercial-CompartirIgual 3.0 España