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dc.contributor.authorTang, R
dc.contributor.authorLi, K
dc.contributor.authorDing, W
dc.contributor.authorWang, Y
dc.contributor.authorZhou, H
dc.contributor.authorFu, G
dc.date.accessioned2020-01-10T17:13:15Z
dc.date.issued2020-01-07
dc.description.abstractTraditional multi-objective evolutionary algorithms treat each objective equally and search randomly in all solution spaces without using preference information. This might reduce the search efficiency and quality of solutions preferred by decision makers, especially when solving problems with complicated properties or many objectives. Three reference point based algorithms which adopt preference information in optimization progress, e.g., R-NSGA-II, r-NSGA-II and g-NSGA-II, have been shown to be effective in finding more preferred solutions in theoretical test problems. However, more efforts are needed to test their effectiveness in real-world problems. This study conducts a comparison of the above three algorithms with a standard algorithm NSGA-II on a reservoir operation problem to demonstrate their performance in improving the search efficiency and quality of preferred solutions. Under the same calculation times of the objective functions, Pareto optimal solutions of the four algorithms are used in the empirical comparison in terms of the approximation to the preferred solutions. Three performance indicators are then adopted for further comparison. Results show that R-NSGA-II and r-NSGA-II can improve the search efficiency and quality of preferred solutions. The convergence and diversity of their solutions in the concerned region are better than NSGA-II, and the closeness degree to the reference point can be increased by 42.8%, and moreover the number of preferred solutions can be increased by more than 3 times when part of objectives are preferred. By contrast, g-NSGA-II shows worse performance. This study exhibits the performance of three reference point based algorithms and provides insights in algorithm selection for multi-objective reservoir optimization problems.en_GB
dc.description.sponsorshipNational Natural Science Foundation of Chinaen_GB
dc.description.sponsorshipUKRI Future Leaders Fellowshipen_GB
dc.identifier.citationPublished online 7 January 2020en_GB
dc.identifier.doi10.1007/s11269-020-02485-9
dc.identifier.grantnumber91747102en_GB
dc.identifier.grantnumberMR/S017062/1en_GB
dc.identifier.grantnumber51709036
dc.identifier.grantnumber91647201
dc.identifier.grantnumber51579027
dc.identifier.urihttp://hdl.handle.net/10871/40364
dc.language.isoenen_GB
dc.publisherSpringer Verlagen_GB
dc.rights.embargoreasonUnder embargo until 7 january 2021 in compliance with publisher policyen_GB
dc.rights© 2020 Springer Natureen_GB
dc.subjectMulti-objective optimizationen_GB
dc.subjectNSGA-IIen_GB
dc.subjectPreferenceen_GB
dc.subjectReservoir operationen_GB
dc.titleReference point based multi-objective optimization of reservoir operation: a comparison of three algorithmsen_GB
dc.typeArticleen_GB
dc.date.available2020-01-10T17:13:15Z
dc.identifier.issn0920-4741
dc.descriptionThis is the author accepted manuscript. The final version is available from Springer verlag via the DOI in this recorden_GB
dc.identifier.journalWater Resources Managementen_GB
dc.rights.urihttp://www.rioxx.net/licenses/all-rights-reserveden_GB
dcterms.dateAccepted2020-01-01
rioxxterms.versionAMen_GB
rioxxterms.licenseref.startdate2020-01-01
rioxxterms.typeJournal Article/Reviewen_GB
refterms.dateFCD2020-01-10T17:09:03Z
refterms.versionFCDAM
refterms.panelBen_GB


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