High-Performance Simulation-Based Optimization

Thomas Bartz-Beielstein, Bogdan Filipič, Peter Korošec, El-Ghazali Talbi (Herausgeber)

Buch | Hardcover

XIII, 291 Seiten

2019
Springer International Publishing (Verlag)
978-3-030-18763-7 (ISBN)

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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.

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.- 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.- A Parallel Island Model for Hypervolume-Based Many-Objective Optimization.- Many-Core Branch-and-Bound for GPU Accelerators and MIC Coprocessors.

Erscheinungsdatum	16.06.2019
Reihe/Serie	Studies in Computational Intelligence
Zusatzinfo	XIII, 291 p. 71 illus., 47 illus. in color.
Verlagsort	Cham
Sprache	englisch
Maße	155 x 235 mm
Gewicht	619 g
Themenwelt	Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
Themenwelt	Technik ► Elektrotechnik / Energietechnik
Schlagworte	Computational Intelligence • High-performance Algorithms • machine learning • Many-Objective Optimization • parallel optimization • surrogate-based optimization
ISBN-10	3-030-18763-2 / 3030187632
ISBN-13	978-3-030-18763-7 / 9783030187637
Zustand	Neuware