Data-Driven Global Optimization Methods and Applications

Huachao Dong, Peng Wang, Jinglu Li

• Publisher: CRC Press
• Year: 2025
• eBook ISBN: 9781040377895
• Topic: Technology & Engineering
• Subtopic: Programming Algorithms
• Index: Technology & Engineering

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.

Table of contents

• Cover
• Half Title
• Title Page
• Copyright Page
• Table of Contents
• List of Figures
• List of Tables
• Preface
• Chapter 1 ◾ Introduction
• Chapter 2 ◾ Data-Driven Optimization Framework
• Chapter 3 ◾ Benchmark Functions for Data-Driven Optimization Methods
• Chapter 4 ◾ MSSR: Multi-Start Space Reduction Surrogate-Based Global Optimization Method
• Chapter 5 ◾ SOCE: Surrogate-Based Optimization with Clustering-Based Space Exploration for Expensive Multimodal Problems
• Chapter 6 ◾ HSOSR: Hybrid Surrogate-Based Optimization Using Space Reduction for Expensive Black-Box Functions
• Chapter 7 ◾ MGOSIC: Multi-Surrogate-Based Global Optimization Using a Score-Based Infill Criterion
• Chapter 8 ◾ SCGOSR: Surrogate-Based Constrained Global Optimization Using Space Reduction
• Chapter 9 ◾ KTLBO: Kriging-Assisted Teaching–Learning-Based Optimization to Solve Computationally Expensive Constrained Problems
• Chapter 10 ◾ KDGO: Kriging-Assisted Discrete Global Optimization for Black-Box Problems with Costly Objective and Constraints
• Chapter 11 ◾ SAGWO: Surrogate-Assisted Gray Wolf Optimization for High-Dimensional, Computationally Expensive Black-Box Problems
• Index