Design of a LEO satellite constellation for the Tehran region using a hybrid GA-PSO optimization algorithm

This paper investigates the critical role of satellite constellations within the burgeoning space economy, highlighting their increasing contribution to global economic output. Subsequently, recognizing the significance of this trend, the study delves into the determination of key orbital design parameters for satellite constellations. Furthermore, by employing GA-PSO advanced optimization algorithms, the complex problem of satellite constellation design is reformulated as an optimization challenge, facilitating the development of an optimized constellation tailored specifically for the Tehran metropolitan region. This approach underscores the potential of advanced computational methods to enhance the efficiency and cost-effectiveness of space-based infrastructure. The simulation was conducted in a two-stage optimization process. Initially, a Genetic Algorithm (GA) was employed to solve a Thirty-variable optimization problem and identify global optima. Subsequently, a hybrid GA-Particle Swarm Optimization (GA-PSO) algorithm utilized these GA-derived global optima as starting points to refine the solution further, searching for local optima within the vicinity of the GA's solutions. Through this process, the hybrid GA-PSO algorithm enhanced the objective function by ۶۱۰ seconds, demonstrably improving the overall performance. This ultimately resulted in the design of a ten-satellite constellation optimized for coverage over Tehran, providing ۱۷ hours of coverage per ۲۴-hour period.

کلیدواژه ها:

satellite design ، GA-PSO