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蚁群算法是一种解决多变量问题的新型启发式仿生算法。本文分析了卫星对地面的覆盖条件,提出用——/(n+1)重覆盖率来评价星座的覆盖性能,建立了以覆盖性能为目标函数的卫星星座参数优化模型。采用蚁群算法对卫星星座参数进行优化,为星座优化问题提供了一种新方法。在连续蚁群算法的基础上对算法进行改进,提出蚂蚁种群数量的自适应准则,有效的提高了搜索范围与收敛速度。通过仿真表明,与其他方法相比,蚁群算法在星座参数优化有着明显的效率。
Ant colony algorithm is a new heuristic bionic algorithm to solve multivariable problems. In this paper, we analyze the satellite coverage conditions on the ground and propose a - / (n + 1) coverage ratio to evaluate the coverage performance of the constellation. A satellite constellation parameter optimization model with coverage as its objective function is established. The use of ant colony algorithm to optimize the satellite constellation parameters provides a new method for constellation optimization problem. On the basis of continuous ant colony algorithm, the algorithm is improved and an adaptive rule of ant population is proposed, which effectively improves the search range and convergence speed. Simulation shows that compared with other methods, ant colony algorithm has obvious efficiency in constellation parameter optimization.