A repair method for differential evolution with combined variants to solve dynamic constrained optimization problems

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dc.contributor.authorAmeca-Alducin, M.
dc.contributor.authorMezura-Montes, E.
dc.contributor.authorCruz-Ramírez, N.
dc.contributor.conferenceAnnual Conference on Genetic and Evolutionary Computation (GECCO 2015) (11 Jul 2015 - 15 Jul 2015 : Madrid, Spain)
dc.date.issued2015
dc.description.abstractRepair methods, which usually require feasible solutions as reference, have been employed by Evolutionary Algorithms to solve constrained optimization problems. In this work, a novel repair method, which does not require feasible solutions as reference and inspired by the differential mutation, is added to an algorithm which uses two variants of differential evolution to solve dynamic constrained optimization problems. The proposed repair method replaces a local search operator with the aim to improve the overall performance of the algorithm in different frequencies of change in the constrained space. The proposed approach is compared against other recently proposed algorithms in an also recently proposed benchmark. The results show that the proposed improved algorithm outperforms its original version and provides a very competitive overall performance with different change frequencies.
dc.description.statementofresponsibilityMaría-Yaneli Ameca-Alducin, Efrén Mezura-Montes and Nicandro Cruz-Ramírez
dc.identifier.citationProceedings of the Annual Conference on Genetic and Evolutionary Computation (GECCO 2015), 2015, pp.241-248
dc.identifier.doi10.1145/2739480.2754786
dc.identifier.isbn1450334725
dc.identifier.isbn9781450334723
dc.identifier.orcidAmeca-Alducin, M. [0000-0002-0099-9032]
dc.identifier.urihttp://hdl.handle.net/2440/112658
dc.language.isoen
dc.publisherAssociation for Computing Machinery
dc.publisher.placeNew York, USA
dc.rights© 2015 ACM
dc.source.urihttps://doi.org/10.1145/2739480.2754786
dc.subjectDifferential Evolution; constraint-handling; dynamic optimization
dc.titleA repair method for differential evolution with combined variants to solve dynamic constrained optimization problems
dc.typeConference paper
pubs.publication-statusPublished

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