基于种群扰动遗传算法的多任务航天测控站资源池调度方法

A scheduling method for multi-task aerospace TT&C ground station resource pools based on population perturbation genetic algorithm

摘要

现代航天测控地面站面临着测控业务种类的多样化和资源需求急剧增加的双重挑战。传统地面站基带处理资源池因其编排相对固化、缺乏灵活性，导致资源利用率和系统性能受到限制。针对上述问题，提出了一种基于种群扰动遗传算法的动态资源调度策略。该算法通过引入全局组合编码方式、精英个体保留策略、群体扰动机制、非传统的多次随机部分交叉以及单点变异与两点变异并行的变异操作，增加了调度灵活性和响应速度。通过系列仿真实验验证，结果表明，相较于传统算法，提出的基于种群扰动遗传算法能快速实现基带处理资源的动态分配，在任务处理效率上取得了显著改进，尤其在处理高负载的任务时表现优异，有效提升了地面站资源的利用率和操作效率。

Abstract

The ground stations of modern aerospace telemetry, tracking, and command (TT&C) systems face the dual challenges of diversification of measurement and control task types and a sharp increase in resource demands. Traditional ground station baseband processing resource pools are limited in resource utilization and system performance due to their relatively solidified scheduling and lack of flexibility. To address the above problems, this paper proposes a dynamic resource scheduling strategy based on a population perturbation genetic algorithm. The algorithm introduces a global combinatorial encoding method, elitist individual preservation strategy, population perturbation mechanism, non-traditional multi-stage random partial crossover, and parallel single-point and two-point mutation operations, thereby enhancing scheduling flexibility and responsiveness. Through a series of simulation experiments, the results demonstrate that compared to traditional algorithms, the proposed population perturbation genetic algorithm can quickly achieve dynamic baseband processing resource allocation, resulting in significant improvements in task processing efficiency ， it performs particularly well in handling high-load tasks, effectively enhancing the utilization and operational efficiency of ground station resources.

关键词

航天测控地面站 / 任务调度 / 动态调度 / 遗传算法

Key words

TT&C ground station / task scheduling / dynamic scheduling / genetic algorithm

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基金资助

国家自然科学基金(62293494)

成都市科技计划资助(2024-JB00-00014-GX)