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Direct multisearch for multiobjective optimization

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dc.contributor.authorCustodio, A. L.
dc.contributor.authorMadeira, JFA
dc.contributor.authorVaz, A. I. F.
dc.contributor.authorVicente, L. N.
dc.date.accessioned2013-01-31T17:19:53Z
dc.date.available2013-01-31T17:19:53Z
dc.date.issued2011
dc.description.abstractIn practical applications of optimization it is common to have several conflicting objective functions to optimize. Frequently, these functions are subject to noise or can be of black-box type, preventing the use of derivative-based techniques. We propose a novel multiobjective derivative-free methodology, calling it direct multisearch (DMS), which does not aggregate any of the objective functions. Our framework is inspired by the search/poll paradigm of direct-search methods of directional type and uses the concept of Pareto dominance to maintain a list of nondominated points (from which the new iterates or poll centers are chosen). The aim of our method is to generate as many points in the Pareto front as possible from the polling procedure itself, while keeping the whole framework general enough to accommodate other disseminating strategies, in particular, when using the (here also) optional search step. DMS generalizes to multiobjective optimization (MOO) all direct-search methods of directional type. We prove under the common assumptions used in direct search for single objective optimization that at least one limit point of the sequence of iterates generated by DMS lies in (a stationary form of) the Pareto front. However, extensive computational experience has shown that our methodology has an impressive capability of generating the whole Pareto front, even without using a search step. Two by-products of this paper are (i) the development of a collection of test problems for MOO and (ii) the extension of performance and data profiles to MOO, allowing a comparison of several solvers on a large set of test problems, in terms of their efficiency and robustness to determine Pareto fronts.por
dc.identifier.citationCUSTODIO, A. L.; MADEIRA, J. F. A.; VAZ, A. I. F.; VICENTE, L. N. - Direct multisearch for multiobjective optimization. SIAM Journal on Optimization. ISSN 1052-6234. Vol. 21, N.º 3 (2011) p. 1109-1140.por
dc.identifier.issn1052-6234
dc.identifier.urihttp://hdl.handle.net/10400.21/2146
dc.language.isoengpor
dc.peerreviewedyespor
dc.publisherSIAM Publicationspor
dc.subjectMultiobjective Optimizationpor
dc.subjectDerivative-Free Optimizationpor
dc.subjectDirect-Search Methodspor
dc.subjectPositive Spanning Setspor
dc.subjectPareto Dominancepor
dc.subjectNonsmooth Calculuspor
dc.subjectPerformance Profilespor
dc.subjectData Profilespor
dc.subjectDerivative-Free Optimizationpor
dc.subjectTest Problem Toolkitpor
dc.subjectDirect Searchpor
dc.subjectEvolutionary Algorithmspor
dc.subjectGenerationpor
dc.titleDirect multisearch for multiobjective optimizationpor
dc.typejournal article
dspace.entity.typePublication
oaire.citation.conferencePlacePhiladelphiapor
oaire.citation.endPage1140por
oaire.citation.issue3por
oaire.citation.startPage1109por
oaire.citation.titleSIAM Journal on Optimizationpor
oaire.citation.volume21por
person.familyNameMadeira
person.givenNameJose Firmino Aguilar
person.identifier.ciencia-id6F1E-DCF0-D6EC
person.identifier.orcid0000-0001-9523-3808
person.identifier.ridN-6918-2016
person.identifier.scopus-author-id7003405549
rcaap.rightsrestrictedAccesspor
rcaap.typearticlepor
relation.isAuthorOfPublicationd495619a-a6ab-4ff5-8e70-3a1351f934dc
relation.isAuthorOfPublication.latestForDiscoveryd495619a-a6ab-4ff5-8e70-3a1351f934dc

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