Multi-objective optimization for attitude maneuver of liquid-filled flexible spacecraft based on improved hierarchical optimization algorithm

Abstract

To solve the multi-objective optimization problem for attitude maneuver of liquid-filled flexible spacecraft, the authors propose a novel algorithm based on an improved hierarchical structure. This improved hierarchical optimization algorithm (IHOA) is composed of elitist non-dominated sorting genetic algorithm (NSGA-II) and bare-bones multi-objective particle swarm optimization based on r-dominance relation (r-BBMOPSO). Bottom layer algorithm NSGA-II provides elitist individuals for top layer algorithm. Top layer algorithm r-BBMOPSO employs r-domination instead of Pareto domination to strengthen selection pressure and guide the search toward the decision maker’s preference. The modification of hierarchical structure has reduced the influence of randomness and accelerated convergence speed while preserving the operating efficiency of the algorithm. The feasibility and effectiveness of IHOA are demonstrated by bench-mark test problems. Also, the simulation results proved that the proposed optimization algorithm IHOA has the capacity to stably generate solutions which are satisfied the decision maker’s demand in the case of a single run. Especially, based on the optimized parameters, the spacecraft can perform well in attitude maneuver tasks.