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Pattern Search optimization with applications on synthesis of linear antenna arrays

机译:模式搜索优化及其在线性天线阵列合成中的应用

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In this work, Pattern Search (PSearch) method is introduced as a direct, efficient and derivative-free optimization tool with the applications on the antenna array synthesis in the antenna engineering. PSearch is a nonrandom method which can be exploited as a direct searching tool for minimization of a function which is not necessarily differentiable, stochastic, or even continuous. Thus, firstly antenna array synthesis is defined as a multi-objective optimization problem with its feasible variable and target spaces. For this aim, maximum amount of the side-lobe suppressions and broadarrow null generations in any desired directions are simultaneously expressed as objectives in the target space while ensuring maximization in the gain performance of the antenna array. At the same time, the inter-element spacings and excitation amplitudes are considered as optimization variables that results in determination of the physical layout and feeding network of the array. In the optimization procedure, a fitness function is defined based on the target and synthesis variable spaces that can be applied into various antenna array designs, combining part by part with the different requirements. Besides convergence is made fast by a seeding process which consists of running "genetic" algorithm once with the random initial values. Finally, the whole PSearch synthesis method is verified by applying into the many linear antenna arrays synthesizes with various multi-objective requirements, and all of the optimized arrays are observed to outperform uniform arrays and representative designs.
机译:在这项工作中,引入了模式搜索(PSearch)方法作为一种直接,高效且无导数的优化工具,并将其应用于天线工程中的天线阵列综合。 PSearch是一种非随机方法,可以用作直接搜索工具以最小化不一定可区分,随机甚至连续的函数。因此,首先将天线阵列综合定义为具有可行变量和目标空间的多目标优化问题。为了这个目的,在确保期望的天线阵列的增益性能最大化的同时,将任意期望方向上的最大的旁瓣抑制量和宽/窄零位产生同时表示为目标空间中的目标。同时,元素间的间距和激发幅度被认为是优化变量,可以确定阵列的物理布局和馈电网络。在优化过程中,根据目标空间和综合变量空间定义适合度函数,可将其应用于各种天线阵列设计中,并结合不同的要求。此外,播种过程可加快收敛速度​​,该播种过程包括以随机初始值运行一次“遗传”算法。最后,通过将具有各种多目标要求的各种线性天线阵列应用到各种线性天线阵列中,验证了整个PSearch合成方法,并观察到所有优化的阵列均优于均匀阵列和代表性设计。

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