Yanjie Song, Yue Zhang, Yonghao Du, Witold Pedrycz, Lining Xing

Collaborative Scheduling for Electromagnetic Detection Satellites

Model and Reinforcement Learning-Based Evolutionary Algorithms

• Broschiertes Buch

Produktbeschreibung

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.

Produktdetails

• Verlag: Elsevier Science
• Seitenzahl: 250
• Erscheinungstermin: 1. März 2026
• Englisch
• Gewicht: 450g
• ISBN-13: 9780443451034
• ISBN-10: 0443451036
• Artikelnr.: 75036897

Autorenporträt

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