摘要
With the rapid development of space technology, orbital spacecraft formation has received great attention from international and domestic academics and industry. Compared with a single monolithic, the orbital spacecraft formation system has many advantages. This paper presents an improved pigeon-inspired optimization（PIO） algorithm for solving the optimal formation reconfiguration problems of multiple orbital spacecraft. Considering that the uniform distribution random searching system in PIO has its own weakness, a modified PIO model adopting Gaussian strategy is presented and the detailed process is also given. Comparative experiments with basic PIO and particle swarm optimization（PSO） are conducted, and the results have verified the feasibility and effectiveness of the proposed Gaussian PIO（GPIO） in solving orbital spacecraft formation reconfiguration problems.
With the rapid development of space technology, orbital spacecraft formation has received great attention from international and domestic academics and industry. Compared with a single monolithic, the orbital spacecraft formation system has many advantages. This paper presents an improved pigeon-inspired optimization（PIO） algorithm for solving the optimal formation reconfiguration problems of multiple orbital spacecraft. Considering that the uniform distribution random searching system in PIO has its own weakness, a modified PIO model adopting Gaussian strategy is presented and the detailed process is also given. Comparative experiments with basic PIO and particle swarm optimization（PSO） are conducted, and the results have verified the feasibility and effectiveness of the proposed Gaussian PIO（GPIO） in solving orbital spacecraft formation reconfiguration problems.
基金
supported by the National Natural Science Foundation of China(Nos.61425008,61333004,61273054)
the Top-Notch Young Talents Program of China
the Aeronautical Science Foundation of China(No.20135851042)
作者简介
Zhang Shujian is currently a student in the School of Automation Science and Electrical Engineering, Beihang University. His current research interests include bio-inspired computation and autonomous flight control.Corresponding author. Tel： ＋86 10 82317318. E-mail address： hbduan@ buaa.edu.cn （H. Duan）.Duan Haibin is currently a professor and PhD supervisor in the School of Automation Science and Electrical Engineering, Beihang University. He is the head of Bio-inspired Autonomous Flight Systems （BAFS） Research Group of Beihang University. His current research interests include bin-inspired computation, autonomous flight control and bio-inspired computer vision. His academic homepage is： http：// hbduan.buaa.edu.cn
