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首页> 外文期刊>Applied Intelligence: The International Journal of Artificial Intelligence, Neural Networks, and Complex Problem-Solving Technologies >A hybrid neural-genetic algorithm for the frequency assignment problem in satellite communications
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A hybrid neural-genetic algorithm for the frequency assignment problem in satellite communications

机译：卫星通信中频率分配问题的混合神经遗传算法

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摘要

A hybrid Neural-Genetic algorithm (NG) is presented for the frequency assignment problem in satellite communications (FAPSC). The goal of this problem is minimizing the cochannel interference between satellite communication systems by rearranging the frequency assignments. Previous approaches to FAPSC show lack of scalability, which leads to poor results when the size of the problem grows. The NG algorithm consists of a Hopfield neural network which manages the problem constraints hybridized with a genetic algorithm for improving the solutions obtained. This separate management of constraints and optimization of objective function gives the NG algorithm the properties of scalability required.We analyze the FAPSC and its formulation, describe and discuss the NG algorithm and solve a set of benchmark problems. The results obtained are compared with other existing approaches in order to show that the NG algorithm is more scalable and performs better than previous algorithms in the FAPSC.
机译：针对卫星通信（FAPSC）中的频率分配问题，提出了一种混合神经遗传算法（NG）。这个问题的目的是通过重新安排频率分配来使卫星通信系统之间的同频道干扰最小化。以前针对FAPSC的方法显示出缺乏可伸缩性，这会在问题规模扩大时导致不良结果。 NG算法由Hopfield神经网络组成，该网络管理与遗传算法混合的问题约束，以改善获得的解。约束的独立管理和目标函数的优化为NG算法提供了所需的可伸缩性。我们分析了FAPSC及其公式，描述和讨论了NG算法，并解决了一系列基准问题。将获得的结果与其他现有方法进行比较，以表明NG算法比FAPSC中的以前算法更具可伸缩性并且性能更好。

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