Pattern Synthesis of Array Antennas Using an Improved Flower Pollination Algorithm

Abstract

An improved flower pollination algorithm (FPA) is introduced to optimize array pattern in pattern synthesis of array antennas. FPA is inspired by flower pollination in nature, most flowering plants carry out long distance cross-pollination through pollinators, such as bees and birds, its behavior obey Levy distribution, so the step length of the FPA also obey Levy distribution. In order to improve the optimization performance of FPA, a flower pollination algorithm combining dynamic convergence factor and golden sine (DGSFPA) is used for pattern synthesis of array antennas in this manuscript. In DGSFPA, dynamic convergence factor is introduced in cross-pollination to improve the algorithm convergence accuracy; gold sine optimization is used in self-pollination to enhance the ability to jump out of the local optimum. In this paper, DGSFPA is used in linear antenna arrays for pattern synthesis and applied for the minimization of the first side lobe lever (SLL) nearest to the main beam and the peak SLL in side lobe region. Two experiments are carried out to verify the effectiveness of DGSFPA in linear array optimization. Experimental results show that DGSFPA provides lower level in peak SLL and the first side lobe level nearest to the main beam, compared with original FPA and grey wolf optimization (GWO) algorithm.