Show simple item record

dc.contributor.authorLi, K
dc.contributor.authorChen, R
dc.contributor.authorSavic, D
dc.contributor.authorYao, X
dc.date.accessioned2019-02-04T10:50:53Z
dc.date.issued2018-11-12
dc.description.abstractDecomposition has become an increasingly popular technique for evolutionary multi-objective optimization (EMO). A decomposition-based EMO algorithm is usually designed to approximate a whole Pareto-optimal front (PF). However, in practice, the decision maker (DM) might only be interested in her/his region of interest (ROI), i.e., a part of the PF. Solutions outside that might be useless or even noisy to the decision-making procedure. Furthermore, there is no guarantee to find the preferred solutions when tackling many-objective problems. This paper develops an interactive framework for the decomposition-based EMO algorithm to lead a DM to the preferred solutions of her/his choice. It consists of three modules, i.e., consultation, preference elicitation and optimization. Specifically, after every several generations, the DM is asked to score a few candidate solutions in a consultation session. Thereafter, an approximated value function, which models the DM's preference information, is progressively learned from the DM's behavior. In the preference elicitation session, the preference information learned in the consultation module is translated into the form that can be used in a decomposition-based EMO algorithm, i.e., a set of reference points that are biased toward to the ROI. The optimization module, which can be any decomposition-based EMO algorithm in principle, utilizes the biased reference points to direct its search process. Extensive experiments on benchmark problems with three to ten objectives fully demonstrate the effectiveness of our proposed method for finding the DM's preferred solutions.en_GB
dc.description.sponsorshipMinistry of Science and Technology of Chinaen_GB
dc.description.sponsorshipScience and Technology Innovation Committee Foundation of Shenzhenen_GB
dc.description.sponsorshipEngineering and Physical Sciences Research Council (EPSRC)en_GB
dc.identifier.citationPublished online 12 November 2018en_GB
dc.identifier.doi10.1109/TFUZZ.2018.2880700
dc.identifier.grantnumber2017YFC0804002en_GB
dc.identifier.grantnumberZDSYS201703031748284en_GB
dc.identifier.grantnumberEP/J017515/1en_GB
dc.identifier.urihttp://hdl.handle.net/10871/35707
dc.language.isoenen_GB
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_GB
dc.rights© 2018 IEEEen_GB
dc.subjectOptimizationen_GB
dc.subjectSociologyen_GB
dc.subjectStatisticsen_GB
dc.subjectApproximation algorithmsen_GB
dc.subjectDecision makingen_GB
dc.subjectComputer scienceen_GB
dc.subjectBenchmark testingen_GB
dc.titleInteractive Decomposition Multi-Objective Optimization via Progressively Learned Value Functionsen_GB
dc.typeArticleen_GB
dc.date.available2019-02-04T10:50:53Z
dc.description This is the author accepted manuscript. The final version is available from IEEE via the DOI in this record en_GB
dc.identifier.journalIEEE Transactions on Fuzzy Systemsen_GB
dc.rights.urihttp://www.rioxx.net/licenses/all-rights-reserveden_GB
dcterms.dateAccepted2018-10-24
rioxxterms.versionAMen_GB
rioxxterms.licenseref.startdate2018-10-24
rioxxterms.typeJournal Article/Reviewen_GB
refterms.dateFCD2019-02-04T10:47:38Z
refterms.versionFCDAM
refterms.dateFOA2019-02-04T10:51:00Z


Files in this item

This item appears in the following Collection(s)

Show simple item record