Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/13816
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dc.contributor.authorLi, M-
dc.contributor.authorGrosan, C-
dc.contributor.authorYang, S-
dc.contributor.authorLiu, X-
dc.contributor.authorYao, X-
dc.date.accessioned2017-01-09T16:20:24Z-
dc.date.available2017-01-09T16:20:24Z-
dc.date.issued2017-
dc.identifier.citationLi, M., Grosan, C., Yang, S., Liu, X. and Yao, X. (2018) 'Multiline Distance Minimization: A Visualized Many-Objective Test Problem Suite', IEEE Transactions on Evolutionary Computation, 22(1), pp. 61-78. doi: 10.1109/TEVC.2017.2655451.en_US
dc.identifier.issn1089-778X-
dc.identifier.urihttps://bura.brunel.ac.uk/handle/2438/13816-
dc.description.abstractStudying the search behavior of evolutionary many objective optimization is an important, but challenging issue. Existing studies rely mainly on the use of performance indicators which, however, not only encounter increasing difficulties with the number of objectives, but also fail to provide the visual information of the evolutionary search. In this paper, we propose a class of scalable test problems, called multi-line distance minimization problem (ML-DMP), which are used to visually examine the behavior of many-objective search. Two key characteristics of the ML-DMP problem are: 1) its Pareto optimal solutions lie in a regular polygon in the two-dimensional decision space, and 2) these solutions are similar (in the sense of Euclidean geometry) to their images in the high-dimensional objective space. This allows a straightforward understanding of the distribution of the objective vector set (e.g., its uniformity and coverage over the Pareto front) via observing the solution set in the two-dimensional decision space. Fifteen well-established algorithms have been investigated on three types of 10 ML-DMP problem instances. Weakness has been revealed across classic multi-objective algorithms (such as Pareto-based, decomposition based and indicator-based algorithms) and even state-of-the-art algorithms designed especially for many-objective optimization. This, together with some interesting observations from the experimental studies, suggests that the proposed ML-DMP may also be used as a benchmark function to challenge the search ability of optimization algorithms.en_US
dc.description.sponsorship10.13039/501100000266-Engineering and Physical Sciences Research Council; 10.13039/501100001809-National Natural Science Foundation of China; 10.13039/501100000288-Royal Society;-
dc.format.mediumPrint-Electronic-
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.rightsThis work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/-
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/-
dc.subjectmany-objective optimizationen_US
dc.subjectevolutionary algorithmsen_US
dc.subjecttest problemsen_US
dc.subjectvisualizationen_US
dc.subjectsearch behavior examinationen_US
dc.titleMulti-Line distance minimization: A visualized many-objective test problem suiteen_US
dc.typeArticleen_US
dc.identifier.doihttps://doi.org/10.1109/TEVC.2017.2655451-
dc.relation.isPartOfIEEE Transactions on Evolutionary Computation-
pubs.publication-statusPublished-
dc.identifier.eissn1941-0026-
Appears in Collections:Dept of Computer Science Research Papers

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