1st Edition
Data-Driven Global Optimization Methods and Applications
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
Biography
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.
