Computational Optimization, Methods and Algorithms

  • Slawomir Koziel
  • Xin-She Yang

Part of the Studies in Computational Intelligence book series (SCI, volume 356)

Table of contents

  1. Front Matter
  2. Xin-She Yang, Slawomir Koziel
    Pages 1-11
  3. Xin-She Yang
    Pages 13-31
  4. Slawomir Koziel, David Echeverría Ciaurri, Leifur Leifsson
    Pages 33-59
  5. Oliver Kramer, David Echeverría Ciaurri, Slawomir Koziel
    Pages 61-83
  6. Christian A. Hochmuth, Jörg Lässig, Stefanie Thiem
    Pages 101-124
  7. Slawomir Koziel, Stanislav Ogurtsov
    Pages 153-178
  8. Leifur Leifsson, Slawomir Koziel
    Pages 179-210
  9. Alfredo Arias-Montaño, Carlos A. Coello Coello, Efrén Mezura-Montes
    Pages 211-240
  10. Amir Hossein Gandomi, Xin-She Yang
    Pages 259-281
  11. Back Matter

About this book

Introduction

Computational optimization is an important paradigm with a wide range of applications. In virtually all branches of engineering and industry, we almost always try to optimize something - whether to minimize the cost and energy consumption, or to maximize profits, outputs, performance and efficiency. In many cases, this search for optimality is challenging, either because of the high computational cost of evaluating objectives and constraints, or because of the nonlinearity, multimodality, discontinuity and uncertainty of the problem functions in the real-world systems. Another complication is that most problems are often NP-hard, that is, the solution time for finding the optimum increases exponentially with the problem size. The development of efficient algorithms and specialized techniques that address these difficulties is of primary importance for contemporary engineering, science and industry.

 

This book consists of 12 self-contained chapters, contributed from worldwide experts who are working in these exciting areas. The book strives to review and discuss the latest developments concerning optimization and modelling with a focus on methods and algorithms for computational optimization. It also covers well-chosen, real-world applications in science, engineering and industry. Main topics include derivative-free optimization, multi-objective evolutionary algorithms, surrogate-based methods, maximum simulated likelihood estimation, support vector machines, and metaheuristic algorithms. Application case studies include aerodynamic shape optimization, microwave engineering, black-box optimization, classification, economics, inventory optimization and structural optimization. This graduate level book can serve as an excellent reference for lecturers, researchers and students in computational science, engineering and industry.

Keywords

Design optimization Design optimization derivative-free optimization derivative-free optimization engineering optimization engineering optimization evolutionary algorithms evolutionary algorithms firefly algorithm firefly algorithm genetic algorithms genetic algorithms gradient-based method gradient-based method

Editors and affiliations

  • Slawomir Koziel
    • 1
  • Xin-She Yang
    • 2
  1. 1.School of Science and Engineering Engineering Optimization & Modeling CenterReykjavik UniversityReykjavikIceland
  2. 2.Mathematics and Scientific ComputingNational Physical LaboratoryTeddingtonUK

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-20859-1
  • Copyright Information Springer Berlin Heidelberg 2011
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-642-20858-4
  • Online ISBN 978-3-642-20859-1
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • About this book