High-Performance Simulation-Based Optimization

✍ Scribed by Thomas Bartz-Beielstein, Bogdan Filipič, Peter Korošec, El-Ghazali Talbi

Publisher: Springer International Publishing
Year: 2020
Tongue: English
Leaves: 298
Series: Studies in Computational Intelligence 833
Edition: 1st ed.
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

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.

✦ Table of Contents

Front Matter ....Pages i-xiii
Front Matter ....Pages 1-1
Infill Criteria for Multiobjective Bayesian Optimization (Michael T. M. Emmerich, Kaifeng Yang, André H. Deutz)....Pages 3-16
Many-Objective Optimization with Limited Computing Budget (Kalyan Shankar Bhattacharjee, Hemant Kumar Singh, Tapabrata Ray)....Pages 17-46
Multi-objective Bayesian Optimization for Engineering Simulation (Joachim van der Herten, Nicolas Knudde, Ivo Couckuyt, Tom Dhaene)....Pages 47-68
Automatic Configuration of Multi-objective Optimizers and Multi-objective Configuration (Leonardo C. T. Bezerra, Manuel López-Ibáñez, Thomas Stützle)....Pages 69-92
Optimization and Visualization in Many-Objective Space Trajectory Design (Hernán Aguirre, Kiyoshi Tanaka, Tea Tušar, Bogdan Filipič)....Pages 93-112
Front Matter ....Pages 113-113
Simulation Optimization Through Regression or Kriging Metamodels (Jack P. C. Kleijnen)....Pages 115-135
Towards Better Integration of Surrogate Models and Optimizers (Tinkle Chugh, Alma Rahat, Vanessa Volz, Martin Zaefferer)....Pages 137-163
Surrogate-Assisted Evolutionary Optimization of Large Problems (Tinkle Chugh, Chaoli Sun, Handing Wang, Yaochu Jin)....Pages 165-187
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
Open Issues in Surrogate-Assisted Optimization (Jörg Stork, Martina Friese, Martin Zaefferer, Thomas Bartz-Beielstein, Andreas Fischbach, Beate Breiderhoff et al.)....Pages 225-244
Front Matter ....Pages 245-245
A Parallel Island Model for Hypervolume-Based Many-Objective Optimization (Raquel Hernández Gómez, Carlos A. Coello Coello, Enrique Alba)....Pages 247-273
Many-Core Branch-and-Bound for GPU Accelerators and MIC Coprocessors (Nouredine Melab, Jan Gmys, Mohand Mezmaz, Daniel Tuyttens)....Pages 275-291

✦ Subjects

Engineering; Computational Intelligence; Control

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