Comparison of Real-coding Genetic Algorithm with Particle Swarm Optimization on the bandgap bandwidth maximization problem

V. Otevrel, L. Oliva
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引用次数: 4

Abstract

In this paper, two global optimization algorithms, particle swarm optimization (PSO) and mean-adaptive real-coding genetic algorithm (MAD-RCGA), are applied to a problem of optimization of non-traditional dielectric electromagnetic bandgap structures (EBG). The problem is formulated in nine dimensions with the goal of finding as large frequency gap between the eight lowest TM bands as possible. Maximizing the frequency gap enables to improve properties of planar patch antenas in wide band.
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实编码遗传算法与粒子群算法在带隙带宽最大化问题上的比较
将粒子群优化算法(PSO)和平均自适应实数编码遗传算法(MAD-RCGA)两种全局优化算法应用于非传媒质电磁带隙结构的优化问题。这个问题是在9个维度上制定的,目标是在8个最低的TM波段之间找到尽可能大的频率间隙。最大限度地提高频率间隙可以提高平面贴片天线在宽带中的性能。
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