R2-Based Hypervolume Contribution Approximation

被引:38
|
作者
Shang, Ke [1 ]
Ishibuchi, Hisao [1 ]
机构
[1] Southern Univ Sci & Technol, Univ Key Lab Evolving Intelligent Syst Guangdong, Dept Comp Sci & Engn, Shenzhen Key Lab Computat Intelligence, Shenzhen 518055, Peoples R China
基金
中国国家自然科学基金;
关键词
Monte Carlo methods; Optimization; Nickel; Approximation methods; Sociology; Indexes; Evolutionary multiobjective optimization (EMO); hypervolume contribution; R2; indicator; ALGORITHM;
D O I
10.1109/TEVC.2019.2909271
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this letter, a new hypervolume contribution approximation method is proposed which is formulated as an R2 indicator. The basic idea of the proposed method is to use different line segments only in the hypervolume contribution region for the hypervolume contribution approximation. Comparing with a traditional method which is based on the R2 indicator to approximate the hypervolume, the new method can directly approximate the hypervolume contribution and will utilize all the direction vectors only in the hypervolume contribution region. The new method, the traditional method, and the Monte Carlo sampling method together with two exact methods are compared through comprehensive experiments. Our results show the advantages of the new method over the other methods. Comparing with the other two approximation methods, the new method achieves the best performance for comparing hypervolume contributions of different solutions and identifying the solution with the smallest hypervolume contribution. Comparing with the exact methods, the new method is computationally efficient in high-dimensional spaces where the exact methods are impractical to use.
引用
收藏
页码:185 / 192
页数:8
相关论文
共 50 条
  • [1] Direction Vector Selection for R2-Based Hypervolume Contribution Approximation
    Shu, Tianye
    Shang, Ke
    Nan, Yang
    Ishibuchi, Hisao
    PARALLEL PROBLEM SOLVING FROM NATURE - PPSN XVII, PPSN 2022, PT II, 2022, 13399 : 110 - 123
  • [2] What is a Good Direction Vector Set for the R2-based Hypervolume Contribution Approximation
    Nan, Yang
    Shang, Ke
    Ishibuchi, Hisao
    GECCO'20: PROCEEDINGS OF THE 2020 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, 2020, : 524 - 532
  • [3] A Two-stage Hypervolume Contribution Approximation Method Based on R2 Indicator
    Nan, Yang
    Shang, Ke
    Ishibuchi, Hisao
    He, Linjun
    2021 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC 2021), 2021, : 2468 - 2475
  • [4] HVC-Net: Deep Learning Based Hypervolume Contribution Approximation
    Shang, Ke
    Liao, Weiduo
    Ishibuchi, Hisao
    PARALLEL PROBLEM SOLVING FROM NATURE - PPSN XVII, PPSN 2022, PT I, 2022, 13398 : 414 - 426
  • [5] A new R2 indicator for better hypervolume approximation
    Shang, Ke
    Ishibuchi, Hisao
    Zhang, Min-Ling
    Liu, Yiping
    GECCO'18: PROCEEDINGS OF THE 2018 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, 2018, : 745 - 752
  • [6] Learning to Approximate: Auto Direction Vector Set Generation for Hypervolume Contribution Approximation
    Shang, Ke
    Shu, Tianye
    Ishibuchi, Hisao
    IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2024, 28 (01) : 105 - 116
  • [7] HV-Net: Hypervolume Approximation Based on DeepSets
    Shang, Ke
    Chen, Weiyu
    Liao, Weiduo
    Ishibuchi, Hisao
    IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2023, 27 (04) : 1154 - 1160
  • [8] An Algorithm for Calculating the Hypervolume Contribution of a Set
    Zhou, Xiuling
    Guo, Ping
    Chen, C. L. Philip
    2012 WORLD AUTOMATION CONGRESS (WAC), 2012,
  • [9] R2-Based Multi/Many-Objective Particle Swarm Optimization
    Diaz-Manriquez, Alan
    Toscano, Gregorio
    Hugo Barron-Zambrano, Jose
    Tello-Leal, Edgar
    COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE, 2016, 2016
  • [10] Empirical Performance of the Approximation of the Least Hypervolume Contributor
    Nowak, Krzysztof
    Martens, Marcus
    Izzo, Dario
    PARALLEL PROBLEM SOLVING FROM NATURE - PPSN XIII, 2014, 8672 : 662 - 671