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dc.contributor.authorGong, Dunwei
dc.contributor.authorXu, Biao
dc.contributor.authorZhang, Yong
dc.contributor.authorGuo, Yinan
dc.contributor.authorYang, Shengxiang
dc.date.accessioned2019-05-23T10:25:09Z
dc.date.available2019-05-23T10:25:09Z
dc.date.issued2019-04-19
dc.identifier.citationGong, D., Xu, B., Zhang, Y., Guo, Y. and Yang, S. (2019) A similarity-based cooperative co-evolutionary algorithm for dynamic interval multi-objective optimization problems. IEEE Transactions on Evolutionary Computation, in press,en
dc.identifier.issn1089-778X
dc.identifier.urihttps://www.dora.dmu.ac.uk/handle/2086/17875
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 interval multi-objective optimization problems (DI-MOPs) are very common in real-world applications. However, there are few evolutionary algorithms that are suitable for tackling DI-MOPs up to date. A framework of dynamic interval multi-objective cooperative co-evolutionary optimization based on the interval similarity is presented in this paper to handle DI-MOPs. In the framework, a strategy for decomposing decision variables is first proposed, through which all the decision variables are divided into two groups according to the interval similarity between each decision variable and interval parameters. Following that, two sub-populations are utilized to cooperatively optimize decision variables in the two groups. Furthermore, two response strategies, rgb0.00,0.00,0.00i.e., a strategy based on the change intensity and a random mutation strategy, are employed to rapidly track the changing Pareto front of the optimization problem. The proposed algorithm is applied to eight benchmark optimization instances rgb0.00,0.00,0.00as well as a multi-period portfolio selection problem and compared with five state-of-the-art evolutionary algorithms. The experimental results reveal that the proposed algorithm is very competitive on most optimization instances.en
dc.language.isoen_USen
dc.publisherIEEE Pressen
dc.subjectMulti-objective optimizationen
dc.subjectDynamic optimizationen
dc.subjectCooperative co-evolutionary optimizationen
dc.subjectInterval similarityen
dc.subjectResponse strategyen
dc.titleA similarity-based cooperative co-evolutionary algorithm for dynamic interval multi-objective optimization problemsen
dc.typeArticleen
dc.identifier.doihttps://doi.org/10.1109/tevc.2019.2912204
dc.peerreviewedYesen
dc.funderOther external funder (please detail below)en
dc.projectid61773384, 61876184, 61876185, 61873105, 61703188, 61573361, 61573362, 61503220, 61673404, 61673331, and 61763026en
dc.cclicenceCC-BY-NCen
dc.date.acceptance2019-04-15
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en
dc.funder.otherNational Natural Science Foundation of Chinaen


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