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Progressively interactive evolutionary multiobjective optimization

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Sinha, Ankur
dc.date.accessioned 2013-10-10T09:46:44Z
dc.date.available 2013-10-10T09:46:44Z
dc.date.issued 2011
dc.identifier.isbn 978-952-60-4052-3
dc.identifier.issn 1799-4934
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/11079
dc.description.abstract A complete optimization procedure for a multi-objective problem essentially comprises of search and decision making. Depending upon how the search and decision making task is integrated, algorithms can be classified into various categories. Following `a decision making after search' approach, which is common with evolutionary multi-objective optimization algorithms, requires to produce all the possible alternatives before a decision can be taken. This, with the intricacies involved in producing the entire Pareto-front, is not a wise approach for high objective problems. Rather, for such kind of problems, the most preferred point on the front should be the target. In this study we propose and evaluate algorithms where search and decision making tasks work in tandem and the most preferred solution is the outcome. For the two tasks to work simultaneously, an interaction of the decision maker with the algorithm is necessary, therefore, preference information from the decision maker is accepted periodically by the algorithm and progress towards the most preferred point is made. Two different progressively interactive procedures have been suggested in the dissertation which can be integrated with any existing evolutionary multi-objective optimization algorithm to improve its effectiveness in handling high objective problems by making it capable to accept preference information at the intermediate steps of the algorithm. A number of high objective un-constrained as well as constrained problems have been successfully solved using the procedures. One of the less explored and difficult domains, i.e., bilevel multiobjective optimization has also been targeted and a solution methodology has been proposed. Initially, the bilevel multi-objective optimization problem has been solved by developing a hybrid bilevel evolutionary multi-objective optimization algorithm. Thereafter, the progressively interactive procedure has been incorporated in the algorithm leading to an increased accuracy and savings in computational cost. The efficacy of using a progressively interactive approach for solving difficult multi-objective problems has, therefore, further been justified en
dc.format.extent vi, 129 s.
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher Aalto University en
dc.publisher Aalto-yliopisto fi
dc.relation.ispartofseries Aalto University publication series. DOCTORAL DISSERTATIONS fi
dc.relation.ispartofseries 17/2011 fi
dc.title Progressively interactive evolutionary multiobjective optimization en
dc.type G5 Artikkeliväitöskirja fi
dc.contributor.school Kauppakorkeakoulu fi
dc.contributor.school School of Business en
dc.contributor.department Liiketoiminnan teknologian laitos fi
dc.identifier.urn URN:ISBN:978-952-60-4052-3
dc.type.dcmitype text en
dc.programme.major Quantitative Methods en
dc.programme.major Taloustieteiden kvantitatiiviset menetelmät fi
dc.type.ontasot Väitöskirja (artikkeli) fi
dc.type.ontasot Doctoral dissertation (article-based) en
dc.contributor.supervisor Korhonen, Pekka, professor fi
dc.opn Branke, Juergen, professor, Warwick Business School, University of Warwick, Great Britain fi
dc.subject.helecon päätöksenteko
dc.subject.helecon optimointi
dc.subject.helecon ohjausjärjestelmät
dc.subject.helecon decision making
dc.subject.helecon optimization
dc.subject.helecon control systems
dc.subject.helecon quantitative methods
dc.date.defence 2011-03-16
dc.dissid 420
dc.identifier.bibid 574301
local.aalto.digifolder Aalto_65181
local.aalto.digiauth ask


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