Constructive cooperative coevolution for large-scale global optimisation

被引:0
|
作者
Emile Glorieux
Bo Svensson
Fredrik Danielsson
Bengt Lennartson
机构
[1] University West,Department of Engineering Science
[2] Chalmers University of Technology,Department of Signals and Systems
来源
Journal of Heuristics | 2017年 / 23卷
关键词
Evolutionary optimisation; Cooperative coevolution; Algorithm design and analysis; Large-scale optimisation;
D O I
暂无
中图分类号
学科分类号
摘要
This paper presents the Constructive Cooperative Coevolutionary (C3\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm {C}^3$$\end{document}) algorithm, applied to continuous large-scale global optimisation problems. The novelty of C3\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm {C}^3$$\end{document} is that it utilises a multi-start architecture and incorporates the Cooperative Coevolutionary algorithm. The considered optimisation problem is decomposed into subproblems. An embedded optimisation algorithm optimises the subproblems separately while exchanging information to co-adapt the solutions for the subproblems. Further, C3\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm {C}^3$$\end{document} includes a novel constructive heuristic that generates different feasible solutions for the entire problem and thereby expedites the search. In this work, two different versions of C3\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm {C}^3$$\end{document} are evaluated on high-dimensional benchmark problems, including the CEC’2013 test suite for large-scale global optimisation. C3\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm {C}^3$$\end{document} is compared with several state-of-the-art algorithms, which shows that C3\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm {C}^3$$\end{document} is among the most competitive algorithms. C3\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm {C}^3$$\end{document} outperforms the other algorithms for most partially separable functions and overlapping functions. This shows that C3\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm {C}^3$$\end{document} is an effective algorithm for large-scale global optimisation. This paper demonstrates the enhanced performance by using constructive heuristics for generating initial feasible solutions for Cooperative Coevolutionary algorithms in a multi-start framework.
引用
收藏
页码:449 / 469
页数:20
相关论文
共 50 条
  • [1] Constructive cooperative coevolution for large-scale global optimisation
    Glorieux, Emile
    Svensson, Bo
    Danielsson, Fredrik
    Lennartson, Bengt
    [J]. JOURNAL OF HEURISTICS, 2017, 23 (06) : 449 - 469
  • [2] Incremental cooperative coevolution for large-scale global optimization
    Mahdavi, Sedigheh
    Rahnamayan, Shahryar
    Shiri, Mohammad Ebrahim
    [J]. SOFT COMPUTING, 2018, 22 (06) : 2045 - 2064
  • [3] Incremental cooperative coevolution for large-scale global optimization
    Sedigheh Mahdavi
    Shahryar Rahnamayan
    Mohammad Ebrahim Shiri
    [J]. Soft Computing, 2018, 22 : 2045 - 2064
  • [4] Improved Constructive Cooperative Coevolutionary Differential Evolution for Large-Scale Optimisation
    Glorieux, Emile
    Svensson, Bo
    Danielsson, Fredrik
    Lennartson, Bengt
    [J]. 2015 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (IEEE SSCI), 2015, : 1703 - 1710
  • [5] Cooperative coevolution for large-scale global optimization based on fuzzy decomposition
    Lin Li
    Wei Fang
    Yi Mei
    Quan Wang
    [J]. Soft Computing, 2021, 25 : 3593 - 3608
  • [6] Cooperative coevolution for large-scale global optimization based on fuzzy decomposition
    Li, Lin
    Fang, Wei
    Mei, Yi
    Wang, Quan
    [J]. SOFT COMPUTING, 2021, 25 (05) : 3593 - 3608
  • [7] Investigation of Improved Cooperative Coevolution for Large-Scale Global Optimization Problems
    Vakhnin, Aleksei
    Sopov, Evgenii
    [J]. ALGORITHMS, 2021, 14 (05)
  • [8] GPU-based cooperative coevolution for large-scale global optimization
    Kelkawi, Ali
    El-Abd, Mohammed
    Ahmad, Imtiaz
    [J]. NEURAL COMPUTING & APPLICATIONS, 2023, 35 (06): : 4621 - 4642
  • [9] GPU-based cooperative coevolution for large-scale global optimization
    Ali Kelkawi
    Mohammed El-Abd
    Imtiaz Ahmad
    [J]. Neural Computing and Applications, 2023, 35 : 4621 - 4642
  • [10] Investigating surrogate-assisted cooperative coevolution for large-Scale global optimization
    De Falco, Ivanoe
    Della Cioppa, Antonio
    Trunfio, Giuseppe A.
    [J]. INFORMATION SCIENCES, 2019, 482 : 1 - 26