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