遗传算法对天线罩结构的优化

利用传输矩阵法，将天线罩等效为均匀多层介质平板，对其功率传输系数作了分析，并利用遗传算法对介质层厚度进行了优化，并以此法对某型天线罩所作的优化结构设计达到了预期效果。     

夹层天线罩壁电性能的多目标遗传优化

提出最小极化差异、最大入射角范围和最大工作频率范围准则,基于这些准则对实际的C-夹层结构的电性能进行了遗传优化,并与基于最大功率透射系数和最小功率反射系数准则的结果进行了比较.所考虑的实际的C-夹层是一种14层结构,其中包括防雨涂层、胶膜、树脂过渡层等.采用页面存储技术建立每一层的材料库从而实现染色体中连续数值变量和离散非数值变量的混合编码.多目标优化通过基于约束条件的单目标优化算法实现.     

基于GA算法的宽带天线罩透波率优化

基于遗传算法(GA),以天线罩的透波率提高为目标,对宽带天线罩的壁厚分布进行优化.首先用射线追踪法建立了天线罩透波率计算的数学模型,在等壁厚天线罩透波率计算结果的基础上,利用遗传算法对天线罩的壁厚分布进行优化,最终实现了宽带天线罩透波性能的提高.     

多层介质金属条带电抗加载平板结构透波特性分析

应用有限元法(FEM)结合Floquet定理分析金属条带电抗加载的多层介质平板结构的电磁特性,并应用该算法模拟电抗加载雷达罩平板结构的透波性能.针对加载周期性、任意截面形状金属条带的多层介质平板结构建立了FEM算法模型并用C 语言编程实现;应用该算法首先分析了加载矩形截面金属条带、以金属为衬底的介质平板的散射特性,数值结果与文献报道结果一致,验证了理论模型与计算程序的正确性;在此基础上,计算了加载细金属丝的雷达罩平板结构的功率传输系数,考察了将金属丝加载于雷达罩壁的不同位置、金属丝的不同间距对电抗加载平板透波性能的影响.数值结果表明,该方法可以有效地分析电抗加载雷达罩平板结构的透波特性.     

GA优化的稀疏分解在雷达目标识别中的应用

特征提取是雷达目标识别系统中很关键的环节,获得一种稳健的目标特征尤为重要。可以利用稀疏分解的方法把雷达高分辨距离像(HRRP)表示在一个超完备Gabor时频字典上,进而提取字典原子的特征参数作为特征向量进行识别;针对匹配追踪算法计算量大的问题,利用遗传算法搜索能力强收敛速度快的优点对OMP算法进行改进。通过对雷达高分辨距离像(HRRP)的识别实验表明,采用Gabor原子提取的特征参数作为特征向量对雷达目标的分类效果比较好。同时,基于GA改进的OMP算法大大降低了参数寻优的计算量。     

利用GA实现非对称稀疏线阵旁瓣电平的优化

该文讨论运用遗传算法综合非对称的稀疏直线阵列(阵元从规则栅格中稀疏)。阵元在中心两侧非对称分布的布阵方式提供了可利用的优化自由度,将更有利于提高稀疏阵列的性能。构造了阵元关于阵中心非对称时优化旁瓣电平的适应度函数,仿真结果表明,无对称约束的阵元排列,不仅可以进一步抑制稀疏线阵的相对旁瓣电平(RSLL),而且当阵列由有向阵元组成时,有益于改善阵列波束扫描过程中RSLL的恶化。     

分析一维光子晶体的传输矩阵法与光学薄膜的菲涅耳系数矩阵法的等效性

一维光子晶体与光学薄膜的结构都具有分层性,为了得它们分析方法的异同,本文比较分析了一维光子晶体能带结构的传输矩阵法与计算光学薄膜分光特性的菲涅耳系数矩阵法,结果发现两种方法在计算一维光子晶体与光学薄膜的反射率与透射率上是等效的,用菲涅耳系数矩阵法计算一维光子晶体的反射率R与入射光波长的变化规律的实例也表明这一点,这两种方法在公式表述中的差别在于位相改变量δ的符号的正负,但不影响计算结果。     

复合左右手传输线平衡点的三点快速优化方法

提出了一种基于遗传算法的快速优化算法,使复合左右手传输线达到平衡状态且拥有较低的回损值.算法使用商用软件分析出两个级联单元在3个邻近频率点的转移矩阵.利用Bloch方法计算出传输线的衰减系数和相移系数,据此提出一种适应度函数,并利用其进行优化.优化过程中采用自适应变异率和移民策略避免了早熟收敛与随机搜索问题;所分析的模型与原模型相比,减少了传输线单元,且无须大规模扫频,从而提高了优化速度.通过比较发现,该算法有更广阔的适用范围和更好的优化效果.     

长周期光纤光栅透射谱与结构参数关系的研究

采用光纤的3层模型,从模式理论和耦合理论出发,分析了长周期光纤光栅(LPG)一阶各次包层模和基模的耦合特性.通过分析指出,以往用来区分HF和EH模的2种方法是不等价的.利用传输矩阵法模拟均匀和非均匀情况下LPG的功率透射谱,分析光栅各结构参数对一阶低次包层模与基模耦合的影响.     

基于遗传算法对二维天线罩瞄准误差的优化

基于物理光学法及表面积分法计算了二维相控阵天线罩的瞄准误差并利用遗传算法对瞄准误差进行优化。首先建立二维相控阵天线一罩系统的数学模型并对瞄准误差进行分析计算,并在此基础上利用遗传算法对天线罩的壁厚分布进行编码以实现对瞄准误差的优化。     

Analysis of an anisotropic dielectric radome

A grooved-dielectric flat radome panel is analyzed in terms of an arbitrary direction of incidence onNplanar slabs each of which is dielectrically anisotropic, homogeneous, and lossless. Using this model for the grooved panel, transmission coefficients of 90 percent or greater over a 10:1 frequency band and over0degto60degincidence angle are predicted and demonstrated experimentally. Measurements are presented on two panels from 3 to 35 GHz.     

多层介质天线罩的数值分析

针对含有多层薄层介质的电大尺寸多尺度问题计算的难点,提出了多层薄层介质的等效阻抗边界条件,该边界条件计入了薄层介质引起的电磁场幅度和相位的变化,可简化含多层薄层介质电磁结构的计算,解决多层天线罩薄层介质引起的剖分奇异化问题,实现多层天线罩对于天线性能影响的数值研究。     

Reliability optimization of series-parallel systems using a genetic algorithm

A problem-specific genetic algorithm (GA) is developed and demonstrated to analyze series-parallel systems and to determine the optimal design configuration when there are multiple component choices available for each of several k-out-of-n:G subsystems. The problem is to select components and redundancy-levels to optimize some objective function, given system-level constraints on reliability, cost, and/or weight. Previous formulations of the problem have implicit restrictions concerning the type of redundancy allowed, the number of available component choices, and whether mixing of components is allowed. GA is a robust evolutionary optimization search technique with very few restrictions concerning the type or size of the design problem. The solution approach was to solve the dual of a nonlinear optimization problem by using a dynamic penalty function. GA performs very well on two types of problems: (1) redundancy allocation originally proposed by Fyffe, Hines, Lee, and (2) randomly generated problem with more complex k-out-of-n:G configurations.     

Performance degradation of dielectric radome covered antennas

Performance is evaluated by a method based on the reciprocity theorem. The asymptotic expression for the radome Green's function that allows the characterization of the radome by itself, independently of any specific antenna, is identified. The concept of the modified aperture distribution, which radiating in free space produces the same pattern as the radome-covered antenna, is introduced. It can be used for the design of the radome stratification and for the optimization of the antenna location. On the basis of the formulation of a computer code has been developed that analyzes the degradation induced by radomes with surfaces and dielectric stratifications that can be defined numerically. Theoretical predictions and experimental results are compared     

Multidisciplinary heat generating logic block placement optimization using genetic algorithm

A multidisciplinary optimization methodology for placement of heat generating semiconductor logic blocks on integrated circuit chips is presented. The methodology includes thermal and wiring length criteria, which are optimized simultaneously using a genetic algorithm. An effective thermal performance prediction methodology based on a superposition method is used to determine the temperature distribution on a silicon chip due to multiple heat generating logic blocks. Using the superposition method, the predicted temperature distribution in the silicon chip is obtained in much shorter time than with a detailed finite element model and with comparable accuracy. The main advantage of the present multidisciplinary design and optimization methodology is its ability to handle multiple design objectives simultaneously for optimized placement of heat generating logic blocks. Capabilities of the present methodology are demonstrated by applying it to several standard benchmarks. The multidisciplinary logic block placement optimization results indicate that the maximum temperature on a silicon chip can be reduced by up to 7.5 °C, compared to the case in which only the wiring length is minimized.     

Design and performance of dynamic voltage restorer using genetic algorithm

In the present scenario, sensitive loads suffer nearly 80% of power quality problems especially due to voltage sag. Dynamic voltage restorer (DVR) finds its perfect application as a compensator to mitigate the voltage sag problem. In this paper, source voltage suffers voltage sag of 30% and load terminal voltage is regulated using proportional and integral (PI) controllers. Synchronous reference frame algorithm is adopted to generate reference load voltage for voltage source converter switching. Optimised values of the gains of PI controllers are achieved using genetic algorithm. Control strategy of the DVR is simulated using MATLAB software and performance of DVR is studied.     

Energy optimization in a smart community grid system using genetic algorithm

A smart community grid is an electrical network, which connects several producers, consumers, and prosumers to share energy in an intelligent and secure way. The main challenges of smart community grid are demand response, demand bidding, dynamic electricity tariffs, demand-side management, and prosumers handling. The current state-of-the-art smart grid decision making is focused on consumers and producers behavior while the aim of this research is to achieve prosumer's different goals in an optimized and intelligent way. A genetic algorithm (GA)-based solution is proposed to share energy in an optimized way without affecting the prosumers' preferences. Six prosumers smart community grids data sets are used to validate the performance of the proposed system. The results show that the proposed method significantly improves the loss of energy sharing without compromising the user's preferences.     

An energy optimization in wireless sensor networks by using genetic algorithm

Wireless sensor networks (WSNs) are used for several commercial and military applications, by collecting, processing and distributing a wide range of data. Maximizing the battery life of WSNs is crucial in improving the performance of WSN. In the present study, different variations of genetic algorithm (GA) method have been implemented independently on energy models for data communication of WSNs with the objective to find out the optimal energy \(\hbox {(E)}\) consumption conditions. Each of the GA methods results in an optimal set of parameters for minimum energy consumption in WSN related to the type of selected energy model for data communication, while the best performance of the GA method [energy consumption \((\hbox {E}=3.49\times 10^{-4}\,\hbox {J})\)] is obtained in WSN for communication distance (d) \({\ge }87\,\hbox {m}\) in between the sensor cluster head and a base station.     

Shape optimization of corrugated coatings under grazing incidence using a genetic algorithm

We report on the use of a genetic algorithm (GA) to design optimal shapes for a corrugated coating under near-grazing incidence. A full-wave electromagnetic solver based on the boundary integral formulation is employed to predict the performance of the coating shape. In our GA implementation, we encode each shape of the coating into a binary chromosome. A two-point crossover scheme involving three chromosomes and a geometrical filter are implemented to achieve efficient optimization. A standard magnetic radar absorbing material (MAGRAM) is used for the absorber coating. We present the optimized coating shapes depending on different polarizations. A physical interpretation for the optimized structure is discussed and the resulting shape is compared to conventional planar and triangular shaped designs. Next, we extend this problem from single to multiobjective optimization by using a Pareto GA. The optimization results with two different objectives, viz. height (or weight) of the coating versus absorbing performance, are presented.