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首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >Genetic Algorithm with Maximum-Minimum Crossover (GA-MMC) Applied in Optimization of Radiation Pattern Control of Phased-Array Radars for Rocket Tracking Systems
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Genetic Algorithm with Maximum-Minimum Crossover (GA-MMC) Applied in Optimization of Radiation Pattern Control of Phased-Array Radars for Rocket Tracking Systems

机译：最大最小交叉遗传算法在火箭跟踪系统相控阵雷达辐射方向图控制中的应用

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摘要

In launching operations, Rocket Tracking Systems (RTS) process the trajectory data obtained by radar sensors. In order to improve functionality and maintenance, radars can be upgraded by replacing antennas with parabolic reflectors (PRs) with phased arrays (PAs). These arrays enable the electronic control of the radiation pattern by adjusting the signal supplied to each radiating element. However, in projects of phased array radars (PARs), the modeling of the problem is subject to various combinations of excitation signals producing a complex optimization problem. In this case, it is possible to calculate the problem solutions with optimization methods such as genetic algorithms (GAs). For this, the Genetic Algorithm with Maximum-Minimum Crossover (GA-MMC) method was developed to control the radiation pattern of PAs. The GA-MMC uses a reconfigurable algorithm with multiple objectives, differentiated coding and a new crossover genetic operator. This operator has a different approach from the conventional one, because it performs the crossover of the fittest individuals with the least fit individuals in order to enhance the genetic diversity. Thus, GA-MMC was successful in more than 90% of the tests for each application, increased the fitness of the final population by more than 20% and reduced the premature convergence.
机译：在发射作业中，火箭跟踪系统（RTS）处理由雷达传感器获得的轨迹数据。为了改善功能和维护，可以通过将抛物线反射器（PR）替换为相控阵（PA）来升级雷达。这些阵列通过调节提供给每个辐射元件的信号来实现辐射图的电子控制。但是，在相控阵雷达（PAR）项目中，对问题的建模会受到激励信号的各种组合的影响，从而产生复杂的优化问题。在这种情况下，可以使用遗传算法（GA）等优化方法来计算问题解决方案。为此，开发了具有最大最小交叉的遗传算法（GA-MMC）方法来控制PA的辐射方向图。 GA-MMC使用具有多个目标的可重配置算法，差分编码和新的交叉遗传算子。该算子与传统算子有不同的方法，因为它执行最适合个体与最不适合个体的交换，以增强遗传多样性。因此，GA-MMC在每种应用的90％以上的测试中都取得了成功，使最终人群的适应度提高了20％以上，并减少了过早的收敛。

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