High-Performance Simulation-Based Optimization

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

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.   

• Computational Intelligence
• Many-Objective Optimization
• Surrogate-Based Optimization
• Parallel Optimization
• High-performance Algorithms
• Machine Learning

• 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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