Dynamic regional splitting planning of remote sensing satellite swarm using parallel genetic PSO algorithm

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Dynamic regional splitting planning of remote sensing satellite swarm using parallel genetic PSO algorithm

Wu, Xiande / Yang, Yuheng / Sun, Yuqi / Xie, Yaen / Song, Xiangshuai / Huang, Bing

Abstract The strips from region splitting method are critical premise of meta-task generation and task planning for satellite swarm. To achieve the regional observation with high coverage and low overlap ratio and satellites usage, a novel region splitting algorithm is designed based on Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithm. In the hybrid algorithm of GA and PSO, these two algorithms are executed paralleled, and then exchange the best genes and individuals periodically, to overcome the disadvantage of GA's poor local searching ability and PSO's low convergence speed. The encoding method and parallel iterative optimal method are given in detail. Finally, a simulation scenario is designed and executed to verify proposed algorithm, and the effects of algorithm parameters are analyzed by simulation results.

Highlights • A region splitting method is presented for satellite swarm. • A hybrid algorithm of PSO and GA is designed to improve planning performance. • The PSO and GA are paralleled and exchanged periodically to save excellent individuals.