Collaborative Scheduling for Electromagnetic Detection Satellites: Model and Reinforcement Learning-Based Evolutionary Algorithms

Collaborative Scheduling for Electromagnetic Detection Satellites: Model and Reinforcement Learning-based Evolutionary Algorithms focuses on the models and algorithms related to electromagnetic detection satellite scheduling problems. Sections provide an overview, including background and current state of development and then move on to the basic model of cooperative scheduling and the framework of learning evolutionary algorithms. Next, the book examines cooperative scheduling of homogeneous electromagnetic detection satellites for stationary target detection and reviews research on cooperative planning of heterogeneous electromagnetic detection satellites for low-speed moving target detection.

Final sections examine cooperative scheduling of heterogeneous electromagnetic detection satellites for high-speed moving target detection. Readers will find this to be an in-depth introduction to electromagnetic detection satellite problems.

• 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

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

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

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

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