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    A many-objective evolutionary algorithm based on rotated grid

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    Main article (1.183Mb)
    Date
    2018-03-03
    Author
    Zou, Juan;
    Fu, Liuwei;
    Yang, Shengxiang;
    Zheng, Jinhua;
    Yu, Guo;
    Hu, Yaru
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    Abstract
    Evolutionary optimization algorithms, a meta-heuristic approach, often encounter considerable challenges in many-objective optimization problems (MaOPs). The Pareto-based dominance loses its effectiveness in MaOPs, which are defined as having more than three objectives. Therefore, a more valid selection method is proposed to balance convergence and distribution. This paper proposes an algorithm using rotary grid technology to solve MaOPs (denoted by RGridEA). The algorithm uses the rotating grid to partition the objective space. Instead of using the Pareto non-dominated sorting strategy to layer the population a novel stratified method is used to enhance convergence effectively and make use of the grid to improve distribution and uniformity. Finally, with the other seven algorithm was tested on the test function DTLZ series analysis, confirming RGridEA is effective in resolving MaOPs.
    Description
    Citation : J. Zou, L. Fu, S. Yang, J. Zheng, G. Yu, and Y. Hu. (2018) A many-objective evolutionary algorithm based on rotated grid. Applied Soft Computing, 67, pp. 596-609
    URI
    http://hdl.handle.net/2086/15612
    DOI
    https://doi.org/10.1016/j.asoc.2018.02.031
    Research Group : Centre for Computational Intelligence
    Research Institute : Institute of Artificial Intelligence (IAI)
    Peer Reviewed : Yes
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    • School of Computer Science and Informatics [3008]

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