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" High-Performance Simulation-Based Optimization / "
Thomas Bartz-Beielstein, Bogdan Filipič, Peter Korošec, El-Ghazali Talbi, editors.
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BL
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| Record Number
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861898
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| Title & Author
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High-Performance Simulation-Based Optimization /\ Thomas Bartz-Beielstein, Bogdan Filipič, Peter Korošec, El-Ghazali Talbi, editors.
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| Publication Statement
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Cham :: Springer,, 2020.
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| Series Statement
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Studies in computational intelligence ;; volume 833
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| Page. NO
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1 online resource (xiii, 291 pages) :: illustrations (some color)
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| ISBN
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3030187640
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: 9783030187644
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3030187632
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9783030187637
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| Contents
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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.
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| Abstract
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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. Thats 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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| Subject
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Mathematical optimization.
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Simulation methods.
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Mathematical optimization.
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Simulation methods.
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| Dewey Classification
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006.3
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| LC Classification
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QA402.5
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| Added Entry
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Bartz-Beielstein, Thomas.
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Filipič, Bogdan.
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Korošec, Peter.
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Talbi, El-Ghazali.
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