Intelligent Evolutionary Optimization introduces biologically-inspired intelligent optimization algorithms to address complex optimization problems and provide practical solutions for tackling combinatorial optimization problems. The book explores efficient search and optimization methods in high-dimensional spaces, particularly for high-dimensional multi-objective optimization problems, offering practical guidance and effective solutions across various domains. Providing practical solutions, methods, and tools to tackle complex optimization problems and enhance modern optimization techniques, this book will be a valuable resource for professionals seeking to enhance their understanding and proficiency in intelligent evolutionary optimization.
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Table of Contents
Part I: Evolutionary Algorithm for Many-Objective Optimization1. Preliminary
2. A New Dominance Relation Based Evolutionary Algorithm for Many-Objective Optimization
3. Balancing Convergence and Diversity in Decomposition-Based Many-Objective Optimizers
4. Objective Reduction in Many-Objective Optimization: Evolutionary Multi-objective Approach and Critical
5. Expensive Multi-objective Evolutionary Optimization Assisted by Dominance Prediction
Part II: Heuristic Algorithm for Flexible Job Shop Scheduling Problem
6. Preliminary
7. A Hybrid Harmony Search Algorithm for the Flexible Job Shop Scheduling Problem
8. Flexible Job Shop Scheduling Using Hybrid Differential Evolution Algorithms
9. An Integrated Search Heuristic for Large-scale Flexible Job Shop Scheduling Problems
10. Multi-objective Flexible Job Shop Scheduling Using Memetic Algorithms
Authors
Hua Xu Tsinghua University, Beijing, China.Hua Xu is a leading expert on Intelligent Natural Interaction and service robots. He is currently a Tenured Associate Professor at Tsinghua University, Editor-in-Chief of the journal, Intelligent Systems with Applications and Associate Editor of Expert Systems with Application. Prof. Xu has authored the books Data Mining: Methodology and Applications (2014), Data Mining: Methods and Applications-Application Cases (2017), Evolutionary Machine Learning (2021), Data Mining: Methodology and Applications (2nd edition) (2022), Natural Interaction for Tri-Co Robots, Volume 1: Human-machine Dialogue Intention Understanding (2022) and Natural Interaction for Tri-Co Robots, Volume 2: Sentiment Analysis of Multimodal Interaction Information (2023), and published more than 140 papers in top-tier international journals and conferences. He is a Core Expert of the No.03 National Science and Technology Major Project of the Ministry of Industry and Information Technology of China, Senior Member of the (CCF), member of CAAI and ACM, Vice Chairman of Tsinghua Collaborative Innovation Alliance of Robotics and Industry, and recipient of numerous awards, including the Second Prize of National Award for Progress in Science and Technology, First Prize for Technological Invention of CFLP and First Prize for Science and Technology Progress of CFLP, etc.
Yuan Yuan Beihang University, Beijing, China. Yuan Yuan is a Professor in the School of Computer Science at Beihang University. He received his Ph.D. in Computer Science from Tsinghua University in 2015. His research interests include computational intelligence, machine learning, intelligent software engineering, and multi-objective optimization. To date, He has published dozens of papers as a first author in top international academic journals and conferences such as IEEE TSE, IEEE TEVC, IEEE TASE, ACM TOSEM, and ACM GECCO, with over 3,000 citations on Google Scholar. As a core member of several projects, he has participated in the Major Science and Technology Program of the 02 Project and the National Natural Science Foundation of China, among others, and has received the first prize of the China Federation of Logistics and Purchasing for Science and Invention.
