Payload configuration for multi-mission navigation constellation based on ant colony algorithm
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摘要: 给出了多任务导航星座载荷配置的一套新算法.首先建立了多任务导航星座载荷配置的优化模型;基于任务要求定义并提出了n＋1重覆盖率来评价星座对地面的覆盖性能;蚁群算法(ACA)是一种新型的模拟蚂蚁觅食行为的仿生启发式算法,将蚁群算法运用到载荷配置的优化当中,修正了启发函数及Ant-Cycle模型使它们能够与星座载荷配置的优化相结合;给出了基于该算法的多任务载荷配置优化框图.仿真结果表明蚁群算法快速有效,优化结果满足任务要求.Abstract: A methodology for multi-mission navigation constellation was presented to optimize the payload configuration via the ant colony algorithm (ACA), which is a heuristic technique based on artificial ant colony searching strategy. An optimization model of payload configuration for multi-mission navigation constellation was built. n+1 coverage ratio was defined and put forward to evaluate constellation coverage performance. Ant colony algorithm which is a novel meta-heuristic inspired on the biological behavior of ants was applied to optimize the payload configuration, and then heuristic value and ant-cycle model were modified to suit the optimization. Then, an optimization framework of payload configuration was given based on ACA. Simulation results show that ACA is quick and effective and the solution of payload configuration can meet the requirements of multi-mission constellation.
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Key words:
- navigation /
- satellites /
- performance /
- optimization /
- algorithms
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