• 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 dynamic multiobjective evolutionary algorithm based on a dynamic evolutionary environment model

    Thumbnail
    View/Open
    Main article (1.027Mb)
    Date
    2018-03-28
    Author
    Zou, Juan;
    Li, Qingya;
    Yang, Shengxiang;
    Zheng, Jinhua;
    Peng, Zhou;
    Pei, Tingrui
    Metadata
    Show attachments and full item record
    Abstract
    Traditional dynamic multiobjective evolutionary algorithms usually imitate the evolution of nature, maintaining diversity of population through different strategies and making the population track the Pareto optimal solution set efficiently after the environmental change. However, these algorithms neglect the role of the dynamic environment in evolution, leading to the lacking of active guieded search. In this paper, a dynamic multiobjective evolutionary algorithm based on a dynamic evolutionary environment model is proposed (DEE-DMOEA). When the environment has not changed, this algorithm makes use of the evolutionary environment to record the knowledge and information generated in evolution, and in turn, the knowledge and information guide the search. When a change is detected, the algorithm helps the population adapt to the new environment through building a dynamic evolutionary environment model, which enhances the diversity of the population by the guided method, and makes the environment and population evolve simultaneously. In addition, an implementation of the algorithm about the dynamic evolutionary environment model is introduced in this paper. The environment area and the unit area are employed to express the evolutionary environment. Furthermore, the strategies of constraint, facilitation and guidance for the evolution are proposed. Compared with three other state-of-the-art strategies on a series of test problems with linear or nonlinear correlation between design variables, the algorithm has shown its effectiveness for dealing with the dynamic multiobjective problems.
    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 : Zou, J., Li, Q., Yang, S., Zheng, J., Peng, Z. and Pei, T. (2018) A dynamic multiobjective evolutionary algorithm based on a dynamic evolutionary environment model. Swarm and Evolutionary Computation, 44, pp. 247-259
    URI
    http://hdl.handle.net/2086/16263
    DOI
    https://doi.org/10.1016/j.swevo.2018.03.010
    ISSN : 2210-6502
    Research Group : Centre for Computational Intelligence
    Research Institute : Institute of Artificial Intelligence (IAI)
    Peer Reviewed : Yes
    Collections
    • School of Computer Science and Informatics [3008]

    Related items

    Showing items related by title, author, creator and subject.

    • Evolutionary programming with q-Gaussian mutation for evolutionary optimization problems. 

      Tinos, Renato; Yang, Shengxiang (Article)
      The use of evolutionary programming algorithms with self-adaptation of the mutation distribution for dynamic optimization problems is investigated in this paper. In the proposed method, the q-Gaussian distribution is ...
    • Evolutionary Algorithms for Static and Dynamic Multiobjective Optimization 

      Jiang, Shouyong (Thesis or dissertation / Doctoral / PhD)
      Many real-world optimization problems consist of a number of conflicting objectives that have to be optimized simultaneously. Due to the presence of multiple conflicting ob- jectives, there is no single solution that can ...
    • A co-evolutionary framework to reducing the gap between business and information technology. 

      Khan, Muhammad Asif (Thesis or dissertation / Doctoral / PhD)
      Over the past few years information technology (IT) and business alignment has become a great concern to organizations. To achieve alignment has become a daunting task for organizations due to rapid changes in business ...

    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