Abstract:

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.

Key words: trajectory optimization, adaptive simulated annealing genetic algorithm, simulated annealing algorithm, genetic algorithm, lunar soft landing, parameterized method