Intelligent Evolutionary Optimization

1st Edition - April 18, 2024
Imprint: Elsevier
Authors: Hua Xu, Yuan Yuan
Language: English
Paperback ISBN:
9 7 8 - 0 - 4 4 3 - 2 7 4 0 0 - 8
eBook ISBN:
9 7 8 - 0 - 4 4 3 - 2 7 4 0 1 - 5

Intelligent Evolutionary Optimization introduces biologically-inspired intelligent optimization algorithms to address complex optimization problems and provide practical soluti… Read more

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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.

Cover image
Title page
Table of Contents
Copyright
List of figures
List of tables
List of algorithms
About the authors
Preface
Part I: Evolutionary algorithm for many-objective optimization
Chapter 1. Preliminary
Abstract
Table of Contents
1.1 Fundamental concepts and basic framework
1.2 Provides an overview of related research
1.3 Conclusion
References
Chapter 2. New dominance relation-based evolutionary algorithm for many-objective optimization
Abstract
Table of Contents
2.1 Introduction
2.2 Preliminaries
2.3 Proposed algorithm: θ-DEA
2.4 Experimental design
2.5 Experimental results
2.6 Conclusion
References
Chapter 3. Balancing convergence and diversity in decomposition-based many-objective optimizers
Abstract
Table of Contents
3.1 Introduction
3.2 Preliminaries and background
3.3 Basic idea
3.4 Proposed algorithms
3.5 Experimental design
3.6 Analysis of the performance of enhanced algorithms
3.7 Comparison with state-of-the-art algorithms
3.8 Conclusion
References
Chapter 4. Objective reduction in many-objective optimization: evolutionary multiobjective approaches and comprehensive analysis
Abstract
Table of Contents
4.1 Introduction
4.2 Preliminaries and background
4.3 Proposed multiobjective approaches
4.4 Analysis of dominance structure- and correlation-based approaches
4.5 Benchmark experiments
4.6 Applications to real-world problems
4.7 Benefits of the proposed approaches
4.8 Conclusion
References
Chapter 5. Expensive multiobjective evolutionary optimization assisted by dominance prediction
Abstract
Table of Contents
5.1 Introduction
5.2 Preliminaries and background
5.3 The proposed algorithm
5.4 Experiments
5.5 Conclusion
References
Summary of part I
Part II: Heuristic algorithm for flexible job shop scheduling problem
Chapter 6. Preliminary
Abstract
Table of Contents
6.1 Dealing with the challenge of scheduling in flexible job-shops with multiple goals
6.2 Research status of multiobjective flexible job-shop scheduling
6.3 Memetic algorithm explained
6.4 Conclusion
References
Chapter 7. A hybrid harmony search algorithm for flexible job shop scheduling problem
Abstract
Table of Contents
7.1 Introduction
7.2 The proposed algorithm
7.3 Experimental details
7.4 Discussion
7.5 Conclusion
References
Chapter 8. Flexible job shop scheduling using hybrid differential evolution algorithms
Abstract
Table of Contents
8.1 Introduction
8.2 Basic differential evolution algorithm
8.3 Proposed hybrid differential evolution for the flexible job shop scheduling problem
8.4 Experimental studies
8.5 Conclusion
References
Chapter 9. An integrated search heuristic for large-scale flexible job shop scheduling problems
Abstract
Table of Contents
9.1 Introduction
9.2 Hybrid harmony search
9.3 Large neighborhood search
9.4 Integrated search heuristic: hybrid harmony search/large neighborhood search
9.5 Experimental study
9.6 Conclusion
References
Chapter 10. Multiobjective flexible job shop scheduling using memetic algorithms
Abstract
Table of Contents
10.1 Introduction
10.2 Background
10.3 Overview of the proposed memetic algorithms
10.4 Exploration using genetic search
10.5 Exploitation using local search
10.6 Experimental studies
10.7 Conclusion
10.8 Summary of this part
References
Summary of part II
Appendix A. Symbol cross-reference table
Index

Hua Xu

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.

Affiliations and expertise

Tsinghua University, Beijing, China

Yuan Yuan

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.

Affiliations and expertise

Beihang University, Beijing, China

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