Table of contents

  1. Front Matter
    Pages i-viii
  2. Leifur Leifsson, Slawomir Koziel, Piotr Kurgan
    Pages 87-111
  3. Adrian Bekasiewicz, Slawomir Koziel, Wlodzimierz Zieniutycz
    Pages 113-147
  4. Abdel-Karim S. O. Hassan, Hany L. Abdel-Malek, Ahmed S. A. Mohamed
    Pages 171-194
  5. Shi Cheng, T. O. Ting, Xin-She Yang
    Pages 241-253
  6. Chin Wei Bong, Xin-She Yang
    Pages 255-268
  7. Christoph Waldhauser, Ronald Hochreiter, Johannes Otepka, Norbert Pfeifer, Sajid Ghuffar, Karolina Korzeniowska et al.
    Pages 269-292
  8. Abhishek Awasthi, Jörg Lässig, Oliver Kramer
    Pages 293-314
  9. Keith Worden, Graeme Manson, Elizabeth J. Cross
    Pages 315-335

About these proceedings


Computational complexity is a serious bottleneck for the design process in virtually any engineering area. While migration from prototyping and experimental-based design validation to verification using computer simulation models is inevitable and has a number of advantages, high computational costs of accurate, high-fidelity simulations can be a major issue that slows down the development of computer-aided design methodologies, particularly those exploiting automated design improvement procedures, e.g., numerical optimization. The continuous increase of available computational resources does not always translate into shortening of the design cycle because of the growing demand for higher accuracy and necessity to simulate larger and more complex systems. Accurate simulation of a single design of a given system may be as long as several hours, days or even weeks, which often makes design automation using conventional methods impractical or even prohibitive. Additional problems include numerical noise often present in the simulation data, possible presence of multiple locally optimum designs, as well as multiple conflicting objectives. In this edited book, various techniques that can alleviate solving computationally expensive engineering design problems are presented. One of the most promising approaches is the use of fast replacement models, so-called surrogates, that reliably represent the expensive, simulation-based model of the system/device of interest but they are much cheaper and analytically tractable. Here, a group of international experts summarize recent developments in the area and demonstrate applications in various disciplines of engineering and science. The main purpose of the work is to provide the basic concepts and formulations of the surrogate-based modeling and optimization paradigm, as well as discuss relevant modeling techniques, optimization algorithms and design procedures. Therefore, this book should be useful to researchers and engineers from any discipline where computationally heavy simulations are used on daily basis in the design process.


CAD Computer-aided design Continuous optimization Engineering optimization Surrogate modeling Surrogate-based optimization

Editors and affiliations

  • Slawomir Koziel
    • 1
  • Leifur Leifsson
    • 2
  • Xin-She Yang
    • 3
  1. 1.Engineering Optimization & Modeling Center, School of Science & EngineeringReykjavik UniversityReykjavikIceland
  2. 2.Engineering Optimization & Modeling Center, School of Science & EngineeringReykjavik UniversityReykjavikIceland
  3. 3.School of Science and TechnologyMiddlesex UniversityLondonUnited Kingdom

Bibliographic information

Industry Sectors
Finance, Business & Banking