With the rapid development of remote sensing technology, multi-satellite autonomous task planning has become a key research direction in satellite Earth observation. The core challenge in this field lies in how to achieve efficient collaborative planning of Earth observation tasks under the conditions of limited resources and diversified observation requirements. Intelligent optimization algorithms, as an effective tool to solve this problem, are continuously promoting technological progress in this field. Firstly, an overview of the importance of multi-satellite collaborative task planning and its current research status at home and abroad will be presented. The observation task planning of satellite constellations not only needs to consider the observation capabilities of satellites, user requirements, and resource constraints but also has to deal with complex issues such as visibility constraints between stars and the Earth and spatial geometric transformations. The complexity of these issues makes it difficult for traditional optimization methods to obtain the optimal solution in polynomial time, while intelligent optimization algorithms provide an effective solution. Next, the application of intelligent optimization algorithms in multi-satellite collaborative task planning will be highlighted, by integrating the latest research findings, including methods for multi-satellite autonomous collaborative task planning towards dynamic regional targets, etc. We will demonstrate how these algorithms can optimize key indicators such as regional coverage rate, immediacy, and the number of strips while meeting user requirements and resource constraints. Furthermore, the challenges faced by intelligent optimization algorithms in practical applications will be explored, including the convergence speed of the algorithms, multi-objective optimization strategies, and the integrated application of algorithms in actual satellite task planning. Finally, the future development of multi-satellite collaborative task planning will be prospected. With the continuous advancement of artificial intelligence and machine learning technologies, intelligent optimization algorithms are expected to play a greater role in multi-satellite collaborative task planning, achieving more efficient and intelligent task planning to meet the growing demand for Earth observation.

Dr. Xin Nie, born in 1983, is an Associate Professor and Master's advisor at the School of Computer Science and Engineering, Wuhan Institute of Technology. Holding a Ph.D. in Computer Software and Theories from Wuhan University, he has made significant contributions to the fields of intelligent optimization algorithms, big data, and smart industrial production lines. Dr. Nie has published over 10 SCI/EI indexed papers, secured 4 national invention patents, and registered 2 software copyrights in past 5 years. He actively engages in teaching courses such as "Software Project Management" and "Programming Fundamentals" for international students. As a competition organizer and mentor, he has led teams to numerous awards in the "China Software Cup" and has participated as a judge in robotics and AI competitions.