A Partitioned Space Projection Particle Swarm Optimization Algorithm for Multi-Satellite Planning

Distributed satellite formation mission planning can simultaneously manage multiple Earth observation missions experiencing time and resource conflicts. However, as the number of satellites and missions increases, these conflicts severely reduce observation benefits and the quality of mission completion. To address this issue, this study proposes a partitioned Space Projection Particle Swarm Optimization (SPPSO) algorithm to adapt the constructed hybrid integer programming model for mission planning. First, the algorithm partitions the population into different search spaces based on fitness levels. Subsequently, it uses a projection strategy based on Fast Fourier Transform (FFT) to reconstruct the population within the search space and employs a perception operator to guide particles with lower fitness toward the optimal space. This approach enhances the convergence speed and effectively reduces the risk of becoming trapped in the local optima. To validate the effectiveness of the SPPSO algorithm, it was compared with state-of-the-art PSO variants and other well-known scheduling algorithms for similar planning problems using international standard test functions. According to the Wilcoxon rank-sum and Friedman test results, the SPPSO algorithm achieved the highest average ranking for both the unimodal and multimodal functions. Furthermore, in the simulation test cases of four different scales (25-100), the SPPSO algorithm consistently achieves the highest observation benefit values and mission completion rates. Compared with the suboptimal algorithm, the SPPSO algorithm improved the observation benefit value and mission completion rate by 6.8% and 7.5%, respectively, for the largest-scale tasks, thus validating its effectiveness in increasing the convergence speed and mitigating the risk of local optima.