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High-performance simulation-based op...

Bartz-Beielstein, Thomas.

High-performance simulation-based optimization

Record Type:	Electronic resources : Monograph/item
Title/Author:	High-performance simulation-based optimizationedited by Thomas Bartz-Beielstein ... [et al.].
other author:	Bartz-Beielstein, Thomas.
Published:	Cham :Springer International Publishing :2020.
Description:	xiii, 291 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Mathematical optimization.
Online resource:	https://doi.org/10.1007/978-3-030-18764-4
ISBN:	9783030187644$q(electronic bk.)

High-performance simulation-based optimization
High-performance simulation-based optimization[electronic resource] /edited by Thomas Bartz-Beielstein ... [et al.]. - Cham :Springer International Publishing :2020. - xiii, 291 p. :ill. (some col.), digital ;24 cm. - Studies in computational intelligence,v.8331860-949X ;. - Studies in computational intelligence ;v. 216..

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.

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.

ISBN: 9783030187644$q(electronic bk.)

Standard No.: 10.1007/978-3-030-18764-4doiSubjects--Topical Terms:

183292
Mathematical optimization.

LC Class. No.: QA402.5

Dewey Class. No.: 006.3

High-performance simulation-based optimization
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505 0 $a 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.
520 $a 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.
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