Trajectory optimization of the exploration of asteroids using swarm intelligent algorithms

As a typical NP-HARD problem, trajectory optimization has become the hot point in aerospace research. Considering the limitation of classical optimization algorithms, variant global optimization algorithms for trajectory optimization are applied in an extensive literature. Based on the parameter optimization of low thrust and impulse maneuver, this paper investigates the difference evolution (DE), particle swarm optimization (PSO), and genetic algorithm (GA) algorithms and generates two hybrid algorithms. DE proves to be superior to other non-hybrid algorithms in trajectory optimization problems. The hybrid algorithm of PSO and DE can improve the optimization performance of DE, which is validated by two given benchmarks. The results in this paper indicate the validity and feasibility of the hybrid algorithms.