dc.contributor.author | Wang, Q | |
dc.contributor.author | Zhou, Q | |
dc.contributor.author | Lei, X | |
dc.contributor.author | Savić, DA | |
dc.date.accessioned | 2018-12-20T13:14:45Z | |
dc.date.issued | 2018-08-29 | |
dc.description.abstract | This article compares three multiobjective evolutionary algorithms (MOEAs) with application to the urban drainage system (UDS) adaptation of a capital city in North China. Particularly, we consider the well-known NSGA-II, the built-in solver in the MATLAB Global Optimization Toolbox (MLOT), and a newly-developed hybrid MOEA called GALAXY. Avariety of parameter combinations of each MOEA is systemically applied to examine their impacts on optimization efficiency. Results suggest that the traditional MOEAs suffer from severe parameterization issues. For NSGA-II, the distribution indexes of crossover and mutation operators were found to have dominant impacts, while the probabilities of the two operators played a secondary role. For MLOT, the two-point and the scattered crossover operators accompanied by the adaptive-feasible mutation operator gained the best Pareto fronts, provided the crossover fraction is set to lower values. In contrast, GALAXY was the most robust and easy-to-use tool among the three MOEAs, owing to its elimination of various associated parameters of searching operators for substantially alleviating the parameterization issues. This study contributes to the literature by showing how to improve the robustness of identifying optimal solutions through better selection of operators and associated parameter settings for real-world UDS applications. | en_GB |
dc.description.sponsorship | National Natural Science Foundation of China | en_GB |
dc.description.sponsorship | Public Welfare Research and Ability Construction Project of Guangdong Province, China | en_GB |
dc.description.sponsorship | Science and Technology Program of Guangzhou, China | en_GB |
dc.description.sponsorship | Water Conservancy Science and Technology Innovation Project of Guangdong Province, China | en_GB |
dc.identifier.citation | Vol. 144 (11), article 04018070 | en_GB |
dc.identifier.doi | 10.1061/(ASCE)WR.1943-5452.0000996 | |
dc.identifier.grantnumber | 51809049 | en_GB |
dc.identifier.grantnumber | 2017A020219003 | en_GB |
dc.identifier.grantnumber | 201804010406 | en_GB |
dc.identifier.grantnumber | 201710 | en_GB |
dc.identifier.uri | http://hdl.handle.net/10871/35235 | |
dc.language.iso | en | en_GB |
dc.publisher | American Society of Civil Engineers | en_GB |
dc.rights | © 2018 American Society of Civil Engineers | en_GB |
dc.subject | Urban drainage system adaptation | en_GB |
dc.subject | Multiobjective evolutionary algorithm | en_GB |
dc.subject | Method selection | en_GB |
dc.subject | Parameter setting | en_GB |
dc.title | Comparison of multiobjective optimization methods applied to urban drainage adaptation problems | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2018-12-20T13:14:45Z | |
dc.identifier.issn | 0733-9496 | |
dc.description | This is the author accepted manuscript. The final version is available from American Society of Civil Engineers via the DOI in this record | en_GB |
dc.identifier.journal | Journal of Water Resources Planning and Management | en_GB |
dc.rights.uri | http://www.rioxx.net/licenses/all-rights-reserved | en_GB |
dcterms.dateAccepted | 2018-05-17 | |
rioxxterms.version | AM | en_GB |
rioxxterms.licenseref.startdate | 2018-08-29 | |
rioxxterms.type | Journal Article/Review | en_GB |
refterms.dateFCD | 2018-12-20T13:11:35Z | |
refterms.versionFCD | AM | |
refterms.dateFOA | 2018-12-20T13:14:50Z | |
refterms.panel | B | en_GB |