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首页> 外文期刊>Engineering Optimization >Distribution replacement for improved genetic algorithm performance on a dynamic spacecraft autonomy problem
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Distribution replacement for improved genetic algorithm performance on a dynamic spacecraft autonomy problem

机译：分布替换可改善动态航天器自主性问题的遗传算法性能

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This article looks at the continuous on-board optimization needed for an autonomous nanosatellite platform to perform collectively within a distributed cluster. The spacecraft needs to continuously maintain its own local behaviour (defined with orthogonal Chebyshev polynomials) using a genetic algorithm. The continual arrival of tasks and the external actions of other spacecraft mean that the problem is highly dynamic in nature. Standard genetic algorithms are based around convergence which dramatically reduces the population diversity hampering performance on both multi-modal and dynamic problems such as this. A new family of distribution replacement operators is presented which have the unique ability to explicitly (rather than probabilistically) control the population diversity in fitness (rather than genome) space. This turns out to be highly beneficial for this dynamic problem and outperforms all other existing replacement operators. This result is mirrored and explained analytically using a simplified problem and a Markov model.
机译：本文着眼于自主纳米卫星平台在分布式集群内集体执行所需的持续机载优化。航天器需要使用遗传算法连续保持自己的局部行为（用正交Chebyshev多项式定义）。任务的不断到达和其他航天器的外部动作意味着该问题在本质上是高度动态的。标准的遗传算法基于收敛，这极大地降低了人口多样性在诸如此类的多模式和动态问题上的表现。提出了一个新的分布替换算子家族，它们具有独特的能力来显式（而不是概率地）控制适应度（而不是基因组）空间中的种群多样性。事实证明，这对于解决此动态问题非常有好处，并且胜过所有其他现有的替代运营商。使用简化问题和马尔可夫模型，可以对结果进行镜像和分析性解释。

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