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

The strips from region splitting method are critical premise of meta-task generation and task planning for sat-ellite 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.

Automated summary

This paper proposes a novel region splitting algorithm based on Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithm to achieve high coverage, low overlap ratio, and efficient satellite utilization in regional observation. The encoding method and parallel iterative optimal method are detailed. Finally, simulation results validate the proposed algorithm and analyze the effects of algorithm parameters.