基于多目标智能优化算法的可重构天线优化与设计

发布时间：2018-03-22 02:07

本文选题：NSGA-II　切入点：多目标粒子群优化　出处：《电子科技大学》2014年硕士论文　论文类型：学位论文

【摘要】：在实际应用中,大多数科学和工程问题都是多目标优化问题,由于各个目标函数之间有可能是不可折衷或者相互冲突的,因此不可能有唯一确定的解,能够使所有的目标同时达到最优,对于这些问题通常优化得到的都是一个非支配(Pareto)最优解集。作为最适应可重构特性的天线结构之一,可重构像素天线(reconfigurable pixel antenna)一般由若干个电小的金属贴片阵列构成,贴片之间通过RF开关彼此连接,通过改变开关的通断状态,能够灵活地构造多种天线形状,从而更易实现天线的可重构性能。但是,由于加载的开关数量较多,可重构像素天线的设计较为复杂,所以必须借助高效的搜索方法来挖掘天线潜在的重构能力。本文主要针对多目标智能优化算法和可重构像素天线进行了若干相关研究,具体工作内容如下:1.提出了一种自适应的带有精英保留策略的快速非支配遗传算法(self-adaptive NSGA-II),通过不同特性的基准测试函数与传统的带有精英保留策略的快速非支配遗传算法(NSGA-II)和多目标粒子群优化算法(MOPSO)进行对比,使用收敛性度量和分布性度量指标对优化结果进行评估,进而证明self-adaptive NSGA-II的高效性。2.使用提出的self-adaptive NSGA-II对一款方向图可重构像素天线进行优化,并与微遗传算法(MGA)的优化结果进行对比,结果表明多目标智能优化算法在天线设计优化中较单目标智能优化算法具有更大的优势。3.在可重构像素天线中,距馈电端口距离不等的开关通断对天线性能的影响不同。为了均衡远近开关对天线可重构的影响,同时减少开关数量,降低天线的复杂性,提出一款非均匀尺寸像素单元的可重构像素天线,并使用self-adaptive NSGA-II对天线开关状态进行优化,使天线在两个工作频率下分别实现六个方向的方向图可重构性能。
[Abstract]:In practical applications, most scientific and engineering problems are multi-objective optimization problems. All the targets can be optimized at the same time. For these problems, the optimal solution set is a non-dominated Pareto optimal solution set, which is one of the most suitable antenna structures for reconfigurable properties. Reconfigurable pixel antenna is generally composed of several small metal patch arrays, which are connected to each other by RF switches and can be flexibly constructed by changing the on-off state of the switches. Therefore, it is easier to realize the reconfigurable performance of the antenna. However, the design of the reconfigurable pixel antenna is more complicated because of the large number of loaded switches. Therefore, it is necessary to mine the potential reconstruction ability of antenna by efficient search method. In this paper, we mainly focus on multi-objective intelligent optimization algorithm and reconfigurable pixel antenna. The main work is as follows: 1. An adaptive fast non-dominated genetic algorithm with elitist retention strategy is proposed, which is self-adaptive NSGA-IIA. By using the benchmark function with different characteristics and the traditional fast non-dominance with elitist retention strategy, this paper proposes an adaptive fast non-dominated genetic algorithm with elitist reservation strategy. The transmission algorithm NSGA-II) and the multi-objective particle swarm optimization algorithm (MOPSO) are compared. The convergence metric and distribution metric are used to evaluate the optimization results, and the efficiency of self-adaptive NSGA-II is proved. 2.Using the proposed self-adaptive NSGA-II to optimize a pattern reconfigurable pixel antenna, Compared with the optimization results of microgenetic algorithm (MGA), the results show that the multi-objective intelligent optimization algorithm has more advantages than the single-objective intelligent optimization algorithm in antenna design optimization. In order to balance the effect of the distance between the far and near switches on the antenna reconfiguration, reduce the number of switches and reduce the complexity of the antenna, the switch with different distance from the feed port has different effects on the antenna performance. A reconfigurable pixel antenna with non-uniform size pixel unit is proposed, and the switching state of the antenna is optimized by using self-adaptive NSGA-II. The reconfigurable performance of the antenna can be realized in six directions at two operating frequencies.
【学位授予单位】：电子科技大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TN820;TP18

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