Abstract
In this paper, an improved particle swarm optimization (PSO) algorithm, which can perform a global search over the search space with a faster convergence speed, is proposed for frequency-selective surface (FSS) design. Four improvement strategies including better particle initialization, acceleration coefficient dynamic adjustment, search space reset, and particle density control are applied to PSO algorithm aiming to accelerate convergence and, at the same time, improve its search capability. For verification, the improved PSO algorithm has been successfully implemented to the square loop FSS design. Then, the optimized results are compared to those obtained by the standard PSO algorithm as well as those existing in the literature, respectively, proving that the improved PSO algorithm has the capacity for accelerating convergence on the premise of ensuring the optimization accuracy.
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