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dc.contributor.authorKianifar, Mohammed R.*
dc.contributor.authorCampean, I. Felician*
dc.contributor.authorWood, Alastair S.*
dc.date.accessioned2016-01-25T11:56:43Z
dc.date.available2016-01-25T11:56:43Z
dc.date.issued2016-08
dc.identifier.citationKianifar MR, Campean IF and Wood AS (2016) Application of Permutation Genetic Algorithm for Sequential Model Building–Model Validation Design of Experiments. Soft Computing. 10(8): 3023-3044.en_US
dc.identifier.urihttp://hdl.handle.net/10454/7701
dc.descriptionYesen_US
dc.description.abstractThe work presented in this paper is motivated by a complex multivariate engineering problem associated with engine mapping experiments, which require efficient Design of Experiment (DoE) strategies to minimise expensive testing. The paper describes the development and evaluation of a Permutation Genetic Algorithm (PermGA) to support an exploration-based sequential DoE strategy for complex real-life engineering problems. A known PermGA was implemented to generate uniform OLH DoEs, and substantially extended to support generation of Model Building–Model Validation (MB-MV) sequences, by generating optimal infill sets of test points as OLH DoEs, that preserve good space filling and projection properties for the merged MB + MV test plan. The algorithm was further extended to address issues with non-orthogonal design spaces, which is a common problem in engineering applications. The effectiveness of the PermGA algorithm for the MB-MV OLH DoE sequence was evaluated through a theoretical benchmark problem based on the Six-Hump-Camel-Back (SHCB) function, as well as the Gasoline Direct Injection (GDI) engine steady state engine mapping problem that motivated this research. The case studies show that the algorithm is effective at delivering quasi-orthogonal space-filling DoEs with good properties even after several MB-MV iterations, while the improvement in model adequacy and accuracy can be monitored by the engineering analyst. The practical importance of this work, demonstrated through the engine case study, also is that significant reduction in the effort and cost of testing can be achieved.en_US
dc.description.sponsorshipThe research work presented in this paper was funded by the UK Technology Strategy Board (TSB) through the Carbon Reduction through Engine Optimization (CREO) project.en_US
dc.language.isoenen_US
dc.relation.isreferencedbyhttp://dx.doi.org/10.1007/s00500-015-1929-5en_US
dc.rights© 2016 The Authors. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.en_US
dc.subjectDesign of experiments; Optimal Latin hypercube; Permutation genetic algorithm; Model based engine calibrationen_US
dc.titleApplication of Permutation Genetic Algorithm for Sequential Model Building–Model Validation Design of Experimentsen_US
dc.status.refereedYesen_US
dc.typeArticleen_US
dc.type.versionAccepted Manuscripten_US


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