Examination of PSO, GA-PSO and ACO algorithms for the design optimization of printed antennas

M. Akila, P. Anusha, M. Sindhu, K. Selvan
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引用次数: 2

Abstract

Particle swarm optimization (PSO), Genetic Algorithm (GA) and Ant colony optimization (ACO) are widely used algorithms for optimization problems in electromagnetics. In this paper ACO, PSO and GA based PSO (GA-PSO) are implemented and compared for optimizing the (i) feed location of a simple patch and (ii) element spacing in a linear array to obtain the desired beamwidth. For the aforementioned problems, ACO seems to offer solutions with lesser computational time.
PSO、GA-PSO和蚁群算法在印刷天线设计优化中的应用研究
粒子群算法(PSO)、遗传算法(GA)和蚁群算法(ACO)是求解电磁学优化问题的常用算法。本文对蚁群算法、粒子群算法和基于遗传算法的粒子群算法(GA-PSO)进行了实现和比较,以优化(i)简单贴片的馈电位置和(ii)线性阵列中的元素间距,以获得所需的波束宽度。对于上述问题,蚁群算法似乎提供了更少计算时间的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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