基于自适应模拟退火遗传算法的月球软着陆轨道优化

Optimization of Lunar Soft Landing Trajectory Based on Adaptive Simulated Annealing Genetic Algorithm

将自适应遗传算法与模拟退火算法相结合,形成一种自适应模拟退火遗传算法。该算法不但具备了自适应遗传算法的强大全局搜索能力,也拥有模拟退火算法的强大局部搜索能力。针对月球软着陆轨道优化的特点,利用一种新的参数化方法将轨道优化问题转换为非线性规划问题,并应用提出的自适应模拟退火遗传算法进行优化。数值结果表明:该算法的收敛速度快,优化精度高,且避免了初值敏感、病态梯度和局部收敛等问题,能够搜索到全局最优轨道。

An adaptive simulated annealing genetic algorithm（ASAGA） by combining adaptive genetic algorithm（AGA） with simulated annealing algorithm（SAA） is develped. The new algorithm provides not only with strong global search capability of AGA, but also with strong local search capability of SAA. For optimization of lunar soft landing trajectory, a new parameterized method is used to convert a trajectory optimization problem into a nonlinear programming problem（NLP）, and then the proposed ASAGA is applied. The simulation results indicate that the ASAGA takes on fast convergence rate and high optimization precision, moreover it avoids many shortcomings such as initial value sensitivity, ill-conditioned gradient and local convergence and so on. It can obtain global optimum trajectory.