• Login
    View Item 
    •   DORA Home
    • Faculty of Computing, Engineering and Media
    • School of Computer Science and Informatics
    • View Item
    •   DORA Home
    • Faculty of Computing, Engineering and Media
    • School of Computer Science and Informatics
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    A similarity-based cooperative co-evolutionary algorithm for dynamic interval multi-objective optimization problems

    Thumbnail
    View/Open
    Main article (1.825Mb)
    Date
    2019-04-19
    Author
    Gong, Dunwei;
    Xu, Biao;
    Zhang, Yong;
    Guo, Yinan;
    Yang, Shengxiang
    Metadata
    Show attachments and full item record
    Abstract
    Dynamic 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.
    Description
    The 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.
    Citation : Gong, 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, 24 (1), pp. 142-156
    URI
    https://www.dora.dmu.ac.uk/handle/2086/17875
    DOI
    https://doi.org/10.1109/tevc.2019.2912204
    ISSN : 1089-778X
    Research Institute : Institute of Artificial Intelligence (IAI)
    Peer Reviewed : Yes
    Collections
    • School of Computer Science and Informatics [2970]

    Submission Guide | Reporting Guide | Reporting Tool | DMU Open Access Libguide | Take Down Policy | Connect with DORA
    DMU LIbrary
     

     

    Browse

    All of DORACommunities & CollectionsAuthorsTitlesSubjects/KeywordsResearch InstituteBy Publication DateBy Submission DateThis CollectionAuthorsTitlesSubjects/KeywordsResearch InstituteBy Publication DateBy Submission Date

    My Account

    Login

    Submission Guide | Reporting Guide | Reporting Tool | DMU Open Access Libguide | Take Down Policy | Connect with DORA
    DMU LIbrary