Hierarchical Optimization Algorithm and Applications of Spacecraft Trajectory Optimization

The pursuit of excellent performance in meta-heuristic algorithms has led to a myriad of extensive and profound research and achievements. Notably, many space mission planning problems are solved with the help of meta-heuristic algorithms, and relevant studies continue to appear. This paper introduces a hierarchical optimization frame in which two types of particles—B-particles and S-particles—synergistically search for the optima. Global exploration relies on B-particles, whose motional direction and step length are designed independently. S-particles are for fine local exploitation near the current best B-particle. Two specific algorithms are designed according to this frame. New variants of classical benchmark functions are used to better test the proposed algo-rithms. Furthermore, two spacecraft trajectory optimization problems, spacecraft multi-impulse orbit transfer and the pursuit-evasion game of two spacecraft, are employed to examine the applica-bility of the proposed algorithms. The simulation results indicate that the hierarchical optimization algorithms perform well on given trials and have great potential for space mission planning.