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The GLOBAL Optimization Algorithm

Newly Updated with Java Implementation and Parallelization

  • Balázs Bánhelyi
  • Tibor Csendes
  • Balázs Lévai
  • László Pál
  • Dániel Zombori

Part of the SpringerBriefs in Optimization book series (BRIEFSOPTI)

Table of contents

  1. Front Matter
    Pages i-ix
  2. Balázs Bánhelyi, Tibor Csendes, Balázs Lévai, László Pál, Dániel Zombori
    Pages 1-5
  3. Balázs Bánhelyi, Tibor Csendes, Balázs Lévai, László Pál, Dániel Zombori
    Pages 7-25
  4. Balázs Bánhelyi, Tibor Csendes, Balázs Lévai, László Pál, Dániel Zombori
    Pages 27-39
  5. Balázs Bánhelyi, Tibor Csendes, Balázs Lévai, László Pál, Dániel Zombori
    Pages 41-67
  6. Balázs Bánhelyi, Tibor Csendes, Balázs Lévai, László Pál, Dániel Zombori
    Pages 69-79
  7. Back Matter
    Pages 81-111

About this book

Introduction

This book explores the updated version of the GLOBAL algorithm which contains improvements for a local search algorithm and new Java implementations. Efficiency comparisons to earlier versions and on the increased speed achieved by the parallelization, are detailed. Examples are provided for students as well as researchers and practitioners in optimization, operations research, and mathematics to compose their own scripts with ease. A GLOBAL manual is presented in the appendix to assist new users with modules and test functions.  
GLOBAL is a successful stochastic multistart global optimization algorithm that has passed several computational tests, and is efficient and reliable for small to medium dimensional global optimization problems. The algorithm uses clustering to ensure efficiency and is modular in regard to the two local search methods it starts with, but it can also easily apply other local techniques. The strength of this algorithm lies in its reliability and adaptive algorithm parameters. The GLOBAL algorithm is free to download also in the earlier Fortran, C, and MATLAB implementations.

Keywords

Global Optimization Java Implementation GLOBAL Algorithm Parallelization local search algorithm the random walk type local search technique nonlinear optimization Derivative-free local search UNIRANDI method UNIRANDI algorithm Error analysis MatLab java Optimizer

Authors and affiliations

  • Balázs Bánhelyi
    • 1
  • Tibor Csendes
    • 2
  • Balázs Lévai
    • 3
  • László Pál
    • 4
  • Dániel Zombori
    • 5
  1. 1.Department of Computational OptimizationUniversity of SzegedSzegedHungary
  2. 2.Department of Computational OptimizationUniversity of SzegedSzegedHungary
  3. 3.NNG IncSzegedHungary
  4. 4.Sapientia Hungarian University of TransylvaniaMiercurea CiucRomania
  5. 5.Department of Computational OptimizationUniversity of SzegedSzegedHungary

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-030-02375-1
  • Copyright Information The Author(s), under exclusive licence to Springer International Publishing AG, part of Springer Nature 2018
  • Publisher Name Springer, Cham
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-3-030-02374-4
  • Online ISBN 978-3-030-02375-1
  • Series Print ISSN 2190-8354
  • Series Online ISSN 2191-575X
  • Buy this book on publisher's site