High-Performance Simulation-Based Optimization
by Thomas Bartz-Beielstein (Editor), Bogdan Filipič (Editor), Peter Korošec (Editor), El-Ghazali Talbi (Editor)

About the Author
Dr. Thomas Bartz-Beielstein is a professor for Applied Mathematics at the Faculty of Computer Science and Engineering Science at TH Köln. He is managing director of the Institute for Data Science, Engineering, and Analytics (IDE+A, see https://idea.f10.th-koeln.de). He invented the SPOTSeven methodology, which defines a process model. It enables systematic optimization of complex real-world problems. He and his team (more than a dozen PhD students) focuses on simulation and process optimization in environmental engineering, energy, water, steel, and plastics industry.
Prof. Bogdan Filipič, Ph.D., senior research associate and head of Computational Intelligence Group at the Department of Intelligent Systems of the Jozef Stefan Institute, Ljubljana, Slovenia, and associate professor of Computer and Information Science at the University of Ljubljana and Jozef Stefan International Postgraduate School.

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.   

Brief contents
Part I Many-Objective Optimization
Infill Criteria for Multiobjective Bayesian Optimization
Many-Objective Optimization with Limited Computing Budget
Multi-objective Bayesian Optimization for Engineering Simulation
Automatic Configuration of Multi-objective Optimizers and Multi-objective Configuration
Optimization and Visualization in Many-Objective Space Trajectory Design
Part II Surrogate-Based Optimization
Simulation Optimization Through Regression or Kriging Metamodels
Towards Better Integration of Surrogate Models and Optimizers
Surrogate-Assisted Evolutionary Optimization of Large Problems
Overview and Comparison of Gaussian Process-Based Surrogate Models for Mixed Continuous and Discrete Variables: Application on Aerospace Design Problems
Open Issues in Surrogate-Assisted Optimization
Part III Parallel Optimization
A Parallel Island Model for Hypervolume-Based Many-Objective Optimization
Many-Core Branch-and-Bound for GPU Accelerators and MIC Coprocessors

Series: Studies in Computational Intelligence (Book 833)
Pages: 291 pages
Publisher: Springer; 1st ed. 2020 edition (June 1, 2019)
Language: English
ISBN-10: 3030187632
ISBN-13: 978-3030187637