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High-Performance Simulation-Based Optimization

  • Book
  • © 2020

Overview

Editors:
  1. Thomas Bartz-Beielstein

    1. TH Köln, Cologne, Germany

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    You can also search for this editor in PubMed   Google Scholar

  2. Bogdan Filipič

    1. Jožef Stefan Institute, Ljubljana, Slovenia

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  3. Peter Korošec

    1. Jožef Stefan Institute, Ljubljana, Slovenia

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  4. El-Ghazali Talbi

    1. University Lille, Lille, France

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  • Presents the state of the art in designing high-performance algorithms that combine machine learning and optimization in order to solve complex problems
  • Provides theoretical treatments and real-world insights gained by the contributing authors, all of whom are leading researchers
  • Offers a comprehensive reference guide for researchers, practitioners, and advanced-level students interested in the theory and practice of using computational intelligence to solve expensive optimization problems

Part of the book series: Studies in Computational Intelligence (SCI, volume 833)

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Table of contents (12 chapters)

  1. Front Matter

    Pages i-xiii
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  2. Many-Objective Optimization

    1. Front Matter

      Pages 1-1
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    2. Infill Criteria for Multiobjective Bayesian Optimization

      • Michael T. M. Emmerich, Kaifeng Yang, André H. Deutz
      Pages 3-16

    3. Many-Objective Optimization with Limited Computing Budget

      • Kalyan Shankar Bhattacharjee, Hemant Kumar Singh, Tapabrata Ray
      Pages 17-46

    4. Multi-objective Bayesian Optimization for Engineering Simulation

      • Joachim van der Herten, Nicolas Knudde, Ivo Couckuyt, Tom Dhaene
      Pages 47-68

    5. Automatic Configuration of Multi-objective Optimizers and Multi-objective Configuration

      • Leonardo C. T. Bezerra, Manuel López-Ibáñez, Thomas Stützle
      Pages 69-92

    6. Optimization and Visualization in Many-Objective Space Trajectory Design

      • Hernán Aguirre, Kiyoshi Tanaka, Tea Tušar, Bogdan Filipič
      Pages 93-112

  3. Surrogate-Based Optimization

    1. Front Matter

      Pages 113-113
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    2. Simulation Optimization Through Regression or Kriging Metamodels

      • Jack P. C. Kleijnen
      Pages 115-135

    3. Towards Better Integration of Surrogate Models and Optimizers

      • Tinkle Chugh, Alma Rahat, Vanessa Volz, Martin Zaefferer
      Pages 137-163

    4. Surrogate-Assisted Evolutionary Optimization of Large Problems

      • Tinkle Chugh, Chaoli Sun, Handing Wang, Yaochu Jin
      Pages 165-187

    5. Overview and Comparison of Gaussian Process-Based Surrogate Models for Mixed Continuous and Discrete Variables: Application on Aerospace Design Problems

      • Julien Pelamatti, Loïc Brevault, Mathieu Balesdent, El-Ghazali Talbi, Yannick Guerin
      Pages 189-224

    6. Open Issues in Surrogate-Assisted Optimization

      • Jörg Stork, Martina Friese, Martin Zaefferer, Thomas Bartz-Beielstein, Andreas Fischbach, Beate Breiderhoff et al.
      Pages 225-244

  4. Parallel Optimization

    1. Front Matter

      Pages 245-245
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    2. A Parallel Island Model for Hypervolume-Based Many-Objective Optimization

      • Raquel Hernández Gómez, Carlos A. Coello Coello, Enrique Alba
      Pages 247-273

    3. Many-Core Branch-and-Bound for GPU Accelerators and MIC Coprocessors

      • Nouredine Melab, Jan Gmys, Mohand Mezmaz, Daniel Tuyttens
      Pages 275-291
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Keywords

About this book

This book presents the state of the art in designing high-performance algorithms that combine simulation and optimization in order to solve complex optimization problems in science and industry, problems that involve time-consuming simulations and expensive multi-objective function evaluations. As traditional optimization approaches are not applicable per se, combinations of computational intelligence, machine learning, and high-performance computing methods are popular solutions. But finding a suitable method is a challenging task, because numerous approaches have been proposed in this highly dynamic field of research.
 
That’s where this book comes in: It covers both theory and practice, drawing on the real-world insights gained by the contributing authors, all of whom are leading researchers. Given its scope, if offers a comprehensive reference guide for researchers, practitioners, and advanced-level students interested in using computational intelligence and machine learning to solve expensive optimization problems.   



Editors and Affiliations

  • TH Köln, Cologne, Germany

    Thomas Bartz-Beielstein

  • Jožef Stefan Institute, Ljubljana, Slovenia

    Bogdan Filipič, Peter Korošec

  • University Lille, Lille, France

    El-Ghazali Talbi

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