Properties and numerical testing of a parallel global optimization algorithm

被引:0
|
作者
Marco Gaviano
Daniela Lera
机构
[1] University of Cagliari,Dipartimento di Matematica e Informatica
来源
Numerical Algorithms | 2012年 / 60卷
关键词
Random search; Global optimization; Parallel computing;
D O I
暂无
中图分类号
学科分类号
摘要
In the framework of multistart and local search algorithms that find the global minimum of a real function f(x), \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$ x \in S\subseteq R^n$\end{document}, Gaviano et alias proposed a rule for deciding, as soon as a local minimum has been found, whether to perform or not a new local minimization. That rule was designed to minimize the average local computational cost \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$eval_1(\cdotp)$\end{document} required to move from the current local minimum to a new one. In this paper the expression of the cost \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$eval_2(\cdotp)$\end{document} of the entire process of getting a global minimum is found and investigated; it is shown that \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$eval_2(\cdotp)$\end{document} has among its components \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$eval_1(\cdotp)$\end{document} and can be only monotonically increasing or decreasing; that is, it exhibits the same property of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$eval_1(\cdotp)$\end{document}. Moreover, a counterexample is given that shows that the optimal values given by \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$eval_1(\cdotp)$\end{document} and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$eval_2(\cdotp)$\end{document} might not agree. Further, computational experiments of a parallel algorithm that uses the above rule are carried out in a MatLab environment.
引用
收藏
页码:613 / 629
页数:16
相关论文
共 50 条
  • [41] Orthogonal chemical reaction optimization algorithm for global numerical optimization problems
    Li, ZhiYong
    Li, Zheng
    Tien Trong Nguyen
    Chen, ShaoMiao
    EXPERT SYSTEMS WITH APPLICATIONS, 2015, 42 (06) : 3242 - 3252
  • [42] An orthogonal electric fish optimization algorithm with quantization for global numerical optimization
    Wang, DanYu
    Liu, Hao
    Tu, LiangPing
    Ding, GuiYan
    SOFT COMPUTING, 2023, 27 (11) : 7259 - 7283
  • [43] A novel marine predator whale optimization algorithm for global numerical optimization
    Su, Ya
    Liu, Yi
    ENGINEERING OPTIMIZATION, 2025,
  • [44] A differential invasive weed optimization algorithm for improved global numerical optimization
    Basak, Aniruddha
    Maity, Dipankar
    Das, Swagatam
    APPLIED MATHEMATICS AND COMPUTATION, 2013, 219 (12) : 6645 - 6668
  • [45] An orthogonal electric fish optimization algorithm with quantization for global numerical optimization
    DanYu Wang
    Hao Liu
    LiangPing Tu
    GuiYan Ding
    Soft Computing, 2023, 27 : 7259 - 7283
  • [46] A Hybrid Mutation Chemical Reaction Optimization Algorithm for Global Numerical Optimization
    Ngambusabongsopa, Ransikarn
    Li, Zhiyong
    Eldesouky, Esraa
    MATHEMATICAL PROBLEMS IN ENGINEERING, 2015, 2015
  • [47] The Parallel Implementation of Interval Global Optimization Algorithm and Its Application
    Qi, Tilong
    Lei, Yongmei
    2014 IEEE 17th International Conference on Computational Science and Engineering (CSE), 2014, : 1658 - 1663
  • [48] A parallel algorithm for reliable nonlinear global optimization with interval arithmetic
    Hu, CY
    PARALLEL AND DISTRIBUTED COMPUTING SYSTEMS, PROCEEDINGS, 2003, : 151 - 156
  • [49] Advanced Parallel Genetic Algorithm with Gene Matrix for Global Optimization
    Hedar, Abdel-Rahman
    Abdelsamee, Amr
    Fouad, Ahmed
    Amin, Sherif Tawfik
    ADVANCED MACHINE LEARNING TECHNOLOGIES AND APPLICATIONS, 2012, 322 : 295 - +
  • [50] A parallel constrained efficient global optimization algorithm for expensive constrained optimization problems
    Qian, Jiachang
    Cheng, Yuansheng
    Zhang, Jinlan
    Liu, Jun
    Zhan, Dawei
    ENGINEERING OPTIMIZATION, 2021, 53 (02) : 300 - 320