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dc.contributor.authorJiang, Shouyongen
dc.contributor.authorKaiser, Marcusen
dc.contributor.authorYang, Shengxiangen
dc.contributor.authorKollias, Stefanosen
dc.contributor.authorKrasnogor, Natalioen
dc.date.accessioned2019-02-20T08:41:29Z
dc.date.available2019-02-20T08:41:29Z
dc.date.issued2019-02-15
dc.identifier.citationJiang, S., Kaiser, M., Yang, S., Kollias, S. and Krasnogor, N. (2019) A scalable test suite for dynamic multiobjective optimisation. IEEE Transactions on Cybernetics, pp.1-13.en
dc.identifier.issn2168-2267
dc.identifier.urihttp://hdl.handle.net/2086/17567
dc.descriptionThe file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.en
dc.description.abstractDynamic multiobjective optimization (DMO) has gained increasing attention in recent years. Test problems are of great importance in order to facilitate the development of advanced algorithms that can handle dynamic environments well. However, many of the existing dynamic multiobjective test problems have not been rigorously constructed and analyzed, which may induce some unexpected bias when they are used for algorithmic analysis. In this paper, some of these biases are identified after a review of widely used test problems. These include poor scalability of objectives and, more important, problematic overemphasis of static properties rather than dynamics making it difficult to draw accurate conclusion about the strengths and weaknesses of the algorithms studied. A diverse set of dynamics and features is then highlighted that a good test suite should have. We further develop a scalable continuous test suite, which includes a number of dynamics or features that have been rarely considered in literature but frequently occur in real life. It is demonstrated with empirical studies that the proposed test suite is more challenging to the DMO algorithms found in the literature. The test suite can also test algorithms in ways that existing test suites cannot.en
dc.language.isoen_USen
dc.publisherIEEEen
dc.subjectAdversarial examplesen
dc.subjectdynamic multiobjective optimization (DMO)en
dc.subjectPareto fronten
dc.subjectscalable test problemsen
dc.titleA scalable test suite for dynamic multiobjective optimizationen
dc.typeArticleen
dc.identifier.doihttps://doi.org/10.1109/tcyb.2019.2896021
dc.researchgroupInstitute of Artificial Intelligence (IAI)en
dc.peerreviewedYesen
dc.funderEPSRC (Engineering and Physical Sciences Research Council)en
dc.funderEPSRC (Engineering and Physical Sciences Research Council)en
dc.projectidEP/K001310/1en
dc.projectidEP/N031962/1en
dc.cclicenceCC-BY-NCen
dc.date.acceptance2019-01-16en
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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