Data-Driven Global Optimization Methods and Applications

<p>This book presents recent advances in data-driven global optimization methods combining theoretical foundations with real-world applications to address complex engineering optimization challenges.</p><p>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.</p><p>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.</p>