基于自适应模拟退火遗传算法的最优Lambert转移

主要研究了航天器采用Lambert二脉冲变轨的优化问题。对于初始位置、目标位置和转移时间都不固定的Lambert二脉冲转移,由于多变量以及方程本身的复杂性,采用传统的优化方法效率低甚至无法求解.采用了自适应遗传算法(AGA),寻求多变量的最优解.同时结合模拟退火算法,得到了自适应模拟退火遗传算法(ASAGA),该算法既具有全局搜索能力,又改善了一般遗传算法的局部寻优能力.通过仿真,比较了遗传算法和自适应模拟退火遗传算法的寻优结果,表明两者寻求最优转移的有效性,以及自适应模拟退火算法具有更强的寻优能力.

Optimal Lambert transfer based on adaptive simulated annealing genetic algorithm

The optimization of Lambert two-impulse transfer was studied.The traditional optimization methods were inefficient or even ineffective for the Lambert transfer with unfixed initial position,final position and transfer time,because of various variables and the complexity of equations.The adaptive genetic algorithm (AGA) was adopted to find the optimal variables.Meanwhile,the adaptive simulated annealing genetic algorithm(ASAGA) was developed by combining AGA and simulated annealing algorithm.The new algorithm not only provided global search capacity,but also improved local search capacity of AGA.The optimization results of AGA and ASAGA were compared.The results validate the effectiveness of two algorithms,and also the stronger search capacity of ASAGA.