Collaborative Scheduling for Electromagnetic Detection Satellites
Model and Reinforcement Learning-Based Evolutionary Algorithms
- 1st Edition - March 1, 2026
- Latest edition
- Authors: Yanjie Song, Yue Zhang, Yonghao Du, Witold Pedrycz, Lining Xing
- Language: English
Collaborative Scheduling for Electromagnetic Detection Satellites: Model and Reinforcement Learning-Based Evolutionary Algorithms delivers a comprehensive study of the schedu… Read more
Collaborative Scheduling for Electromagnetic Detection Satellites: Model and Reinforcement Learning-Based Evolutionary Algorithms delivers a comprehensive study of the scheduling challenges faced by electromagnetic detection satellites. Centered on both theoretical models and practical algorithmic solutions, this book is essential for understanding cooperative satellite operations. It is organized into five distinct sections covering background information, foundational models, and advanced evolutionary algorithm frameworks. Readers will explore scheduling strategies for both homogeneous and heterogeneous satellite systems, addressing the detection needs for stationary, low-speed, and high-speed moving targets. The book offers a thorough introduction to current developments in this rapidly evolving field.
In addition, the book emphasizes the unique challenges presented by different satellite configurations and target dynamics. It explores the use of reinforcement learning to improve evolutionary scheduling algorithms, showcasing how cooperative planning adapts to varying detection scenarios.
In addition, the book emphasizes the unique challenges presented by different satellite configurations and target dynamics. It explores the use of reinforcement learning to improve evolutionary scheduling algorithms, showcasing how cooperative planning adapts to varying detection scenarios.
- Highlights Collaborative Scheduling for Electromagnetic Detection Satellites
- Provides the first book to address the problem of scheduling for electromagnetic detection satellites
- Presents a new model of construction and algorithms
- Contains a large volume of pseudo-code for readers to use
- Includes simulation experiments that can be used by researchers for comparative reference
Researchers and engineers in the fields of satellite operation and control and operations research
1. Introduction
1.1 Background
1.2 Meaning of the Research
1.3 Related work
1.4 Main Content and Innovations
1.5 Structure of this Book
2. Basic Model and Learning-based Evolutionary Algorithm Framework for Electromagnetic Detection Satellite Cooperative Scheduling Problem
2.1 Overview of Cooperative Scheduling for Electromagnetic Detection Satellites
2.2 Overview of Industrial Application processing Flow
2.3 Basic Model of Cooperative Detection Scheduling
2.4 Learning-based Evolutionary Algorithm Framework
2.5 Chapter Summary
3. Homogeneous Electromagnetic Detection Satellites Cooperative Scheduling Model and Algorithm for Stationary Target Detection
3.1 Problem Description and Analysis
3.2 Stationary Target Cooperative Detection Scheduling Model
3.3 Adaptive Operator Selection based Evolutionary Algorithm (AOSEA)
3.4 Simulation Experiments and Analysis
3.5 Chapter Summary
4. Heterogeneous Electromagnetic Detection Satellites Cooperative Scheduling Model and Algorithm for Low-speed Moving Target Detection
4.1 Problem Description
4.2 Low-speed Moving Target Cooperative Detection Scheduling Model
4.3 Solving Algorithm
4.4 Simulation Experiments
4.5 Chapter Summary
5. Heterogeneous Electromagnetic Detection Satellites Cooperative Scheduling Model and Algorithm for High-speed Moving Target Detection
5.1 Problem Description and Analysis
5.2 High-speed Moving Target Cooperative Detection Scheduling Model
5.3 Learning Adaptive-based Evolutionary Algorithm (LAEA)
5.4 Simulation Experiments and Analysis
5.5 Chapter Summary
6. Summary and Future Work
6.1 Summary of the Book
6.2 Future Work Conclusion
1.1 Background
1.2 Meaning of the Research
1.3 Related work
1.4 Main Content and Innovations
1.5 Structure of this Book
2. Basic Model and Learning-based Evolutionary Algorithm Framework for Electromagnetic Detection Satellite Cooperative Scheduling Problem
2.1 Overview of Cooperative Scheduling for Electromagnetic Detection Satellites
2.2 Overview of Industrial Application processing Flow
2.3 Basic Model of Cooperative Detection Scheduling
2.4 Learning-based Evolutionary Algorithm Framework
2.5 Chapter Summary
3. Homogeneous Electromagnetic Detection Satellites Cooperative Scheduling Model and Algorithm for Stationary Target Detection
3.1 Problem Description and Analysis
3.2 Stationary Target Cooperative Detection Scheduling Model
3.3 Adaptive Operator Selection based Evolutionary Algorithm (AOSEA)
3.4 Simulation Experiments and Analysis
3.5 Chapter Summary
4. Heterogeneous Electromagnetic Detection Satellites Cooperative Scheduling Model and Algorithm for Low-speed Moving Target Detection
4.1 Problem Description
4.2 Low-speed Moving Target Cooperative Detection Scheduling Model
4.3 Solving Algorithm
4.4 Simulation Experiments
4.5 Chapter Summary
5. Heterogeneous Electromagnetic Detection Satellites Cooperative Scheduling Model and Algorithm for High-speed Moving Target Detection
5.1 Problem Description and Analysis
5.2 High-speed Moving Target Cooperative Detection Scheduling Model
5.3 Learning Adaptive-based Evolutionary Algorithm (LAEA)
5.4 Simulation Experiments and Analysis
5.5 Chapter Summary
6. Summary and Future Work
6.1 Summary of the Book
6.2 Future Work Conclusion
- Edition: 1
- Latest edition
- Published: March 1, 2026
- Language: English
YS
Yanjie Song
Dr Yanjie Song is an Assistant Professor at the National Engineering Research Center of Maritime Navigation System, Dalian Maritime University in China. He has published more than 50 papers, authored four academic books and been awarded nine National Invention Patents. He is the Associate Editor of the International Journal on Interactive Design and Manufacturing, Guest Editor of the Swarm and Evolutionary Computation and Guest Editor of the Computers & Electrical Engineering. He is also the reviewer of the IEEE Transactions on Evolutionary Computation, IEEE Transactions on Cybernetics, Applied Soft Computing, Neural Computing and Applications and more than 30 other journals. His research interests include computational intelligence, evolutionary algorithm, combinatorial optimization, and deep reinforcement learning
Affiliations and expertise
Assistant Professor, National Engineering Research Center, Maritime Navigation System, Dalian Maritime University, ChinaYZ
Yue Zhang
Yue Zhang is undertaking PhD research at the School of Reliability and Systems Engineering, Beihang University, Beijing, China. In 2023, she received national funding to pursue a joint Ph.D. program at the Department of Industrial and Systems Engineering, National University of Singapore, Singapore. Her research focus is on the O&M of swarm systems under uncertainty: modeling, algorithms, and validation. She has published over 20 journal papers and is a reviewer for several leading journals, including IEEE Transactions on Vehicular Technology, Expert Systems with Applications, Renewable & Sustainable Energy Reviews, Swarm and Evolutionary Computation
Affiliations and expertise
Beihang University, ChinaYD
Yonghao Du
Dr Yonghao Du is currently an associate professor with the College of Systems Engineering, NUDT, China. His research interests include intelligent optimization, resource scheduling, and scheduling. His satellite scheduling engine and algorithms introduced in this book have been applied to real-world satellites operation and management
Affiliations and expertise
Associate Professor, College of Systems Engineering, NUDT, ChinaWP
Witold Pedrycz
Professor Witold Pedrycz (IEEE Life Fellow) is Professor in the Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada. He is also with the Systems Research Institute of the Polish Academy of Sciences, Warsaw, Poland. Dr. Pedrycz is a foreign member of the Polish Academy of Sciences and a Fellow of the Royal Society of Canada. He is a recipient of several awards including Norbert Wiener award from the IEEE Systems, Man, and Cybernetics Society, IEEE Canada Computer Engineering Medal, a Cajastur Prize for Soft Computing from the European Centre for Soft Computing, a Killam Prize, a Fuzzy Pioneer Award from the IEEE Computational Intelligence Society, and 2019 Meritorious Service Award from the IEEE Systems Man and Cybernetics Society. His main research directions involve computational intelligence, granular computing, and machine learning, among others
Affiliations and expertise
University of Alberta, Edmonton, CanadaLX
Lining Xing
Affiliations and expertise
KINDI Centre for Computing Research, Qatar University