Abstract: Aiming at the problems of computing complexity, long time-consuming and low accuracy in the process of antenna optimization design, a particle swarm optimization algorithm based on chaotic ergodic search is proposed in this paper. In order to improve the rationality of the selection of the initial population of the traditional particle swarm optimization algorithm and enhance the diversity of particles in the iterative process of the algorithm, this algorithm introduces a uniform Logistic chaotic map into the traditional particle swarm optimization algorithm, so as to improve the convergence speed and optimization performance of the particle swarm optimization more effectively. In this paper, the Logistic map is selected in the chaotic map, and the analysis shows that although it has good long-term periodicity and initial value sensitivity, the data does not obey the uniform distribution, which makes the omission range when the sequence value is small, which reduces the chaos. The efficiency of traversal, in view of this shortcoming, this paper proposes a method of homogenizing Logistic, and deduces and analyzes it. It is concluded that this homogenized Logistic method has better randomness and can better reflect the characteristics of uniform distribution of data. Further, based on the optimization algorithm, the rectangular microstrip antennas with two feeding modes are optimized, and the antenna size obtained by empirical formula is modeled and analyzed in HFSS software. The research results show that the optimization algorithm in this paper can bring faster convergence speed and more accurate optimization results for antenna optimization.Abstract: Aiming at the problems of computing complexity, long time-consuming and low accuracy in the process of antenna optimization design, a particle swarm optimization algorithm based on chaotic ergodic search is proposed in this paper. In order to improve the rationality of the selection of the initial population of the traditional particle swarm optimi...Show More
