
Data-Driven Global Optimization Methods and Applications
CRC Press
1st Edition
Published on 14. July 2025
Book
Hardback
324 pages
978-1-041-06575-3 (ISBN)
Description
This book presents recent advances in data-driven global optimization methods, combining theoretical foundations with real-world applications to address complex engineering optimization challenges.
The book begins with an overview of the state of the art, key technologies and standard benchmark problems in the field. It then delves into several innovative approaches: space reduction-based, hybrid surrogate model-based and multi-surrogate model-based global optimization, followed by surrogate-assisted constrained global optimization, discrete global optimization and high-dimensional global optimization. These methods represent a variety of optimization techniques that excel in both optimization capability and efficiency, making them ideal choices for complex engineering optimization problems. Through benchmark test problems and real-world engineering applications, the book illustrates the practical implementation of these methods, linking established theories with cutting-edge research in industrial and engineering optimization.
Both a professional book and an academic reference, this title will provide valuable insights for researchers, students, engineers and practitioners in a variety of fields, including optimization methods and algorithms, engineering design and manufacturing and artificial intelligence and machine learning.
The book begins with an overview of the state of the art, key technologies and standard benchmark problems in the field. It then delves into several innovative approaches: space reduction-based, hybrid surrogate model-based and multi-surrogate model-based global optimization, followed by surrogate-assisted constrained global optimization, discrete global optimization and high-dimensional global optimization. These methods represent a variety of optimization techniques that excel in both optimization capability and efficiency, making them ideal choices for complex engineering optimization problems. Through benchmark test problems and real-world engineering applications, the book illustrates the practical implementation of these methods, linking established theories with cutting-edge research in industrial and engineering optimization.
Both a professional book and an academic reference, this title will provide valuable insights for researchers, students, engineers and practitioners in a variety of fields, including optimization methods and algorithms, engineering design and manufacturing and artificial intelligence and machine learning.
More details
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
Professional and scholarly
Academic, Postgraduate, Professional Practice & Development, Professional Reference, Professional Training, Undergraduate Advanced, and Undergraduate Core
Illustrations
69 s/w Tabellen, 126 s/w Abbildungen, 126 s/w Zeichnungen
69 Tables, black and white; 126 Line drawings, black and white; 126 Illustrations, black and white
Dimensions
Height: 240 mm
Width: 161 mm
Thickness: 23 mm
Weight
690 gr
ISBN-13
978-1-041-06575-3 (9781041065753)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions

Huachao Dong | Peng Wang | Jinglu Li
Data-Driven Global Optimization Methods and Applications
E-Book
07/2025
CRC Press
€158.99
Available for download

Huachao Dong | Peng Wang | Jinglu Li
Data-Driven Global Optimization Methods and Applications
E-Book
07/2025
CRC Press
€158.99
Available for download
Persons
Huachao Dong is Associate Professor at the School of Marine Science and Technology at Northwestern Polytechnical University, China. His research includes underwater vehicle design, digital design, multidisciplinary optimization, digital twins for underwater vehicles and data-driven global optimization, with over 50 peer-reviewed papers and 1 book published.
Peng Wang is Professor at the School of Marine Science and Technology at Northwestern Polytechnical University, China. His research focuses on surrogate-based design optimization, multidisciplinary design optimization, multicriteria decision-making and the design of underwater vehicles, with over 150 peer-reviewed papers and 6 books published.
Jinglu Li is an assistant researcher at Harbin Engineering University, China. His research includes underwater vehicle design, multidisciplinary optimization, digital twins and data-driven global optimization and he has published over 20 peer-reviewed papers.
Peng Wang is Professor at the School of Marine Science and Technology at Northwestern Polytechnical University, China. His research focuses on surrogate-based design optimization, multidisciplinary design optimization, multicriteria decision-making and the design of underwater vehicles, with over 150 peer-reviewed papers and 6 books published.
Jinglu Li is an assistant researcher at Harbin Engineering University, China. His research includes underwater vehicle design, multidisciplinary optimization, digital twins and data-driven global optimization and he has published over 20 peer-reviewed papers.
Content
1. Introduction 2. Data-Driven Optimization Framework 3. Benchmark Functions for Data-Driven Optimization Methods 4. MSSR: Multi-Start Space Reduction Surrogate-Based Global Optimization Method 5. SOCE: Surrogate-Based Optimization with Clustering-Based Space Exploration for Expensive Multimodal Problems 6. HSOSR: Hybrid Surrogate-Based Optimization Using Space Reduction for Expensive Black-Box Functions 7. MGOSIC: Multi-Surrogate-Based Global Optimization Using a Score-Based Infill Criterion 8. SCGOSR: Surrogate-Based Constrained Global Optimization Using Space Reduction 9. KTLBO: Kriging-Assisted Teaching-Learning-Based Optimization to Solve Computationally Expensive Constrained Problems 10. KDGO: Kriging-Assisted Discrete Global Optimization for Black-Box Problems with Costly Objective and Constraints 11. SAGWO: Surrogate-Assisted Grey Wolf Optimization for High-Dimensional, Computationally Expensive Black-Box Problems