A Hybrid Optimization Algorithm and Its Application for Conformal Array Pattern Synthesis

Abstract

Investigations on conformal phased array pattern synthesis using a novel hybrid evolutionary algorithm are presented. First, in order to overcome the drawbacks of the standard genetic algorithm (GA) and the particle swarm optimization (PSO), an improved genetic algorithm (IGA) and an improved particle swarm optimization (IPSO) algorithm are proposed by introducing novel mechanisms. Then, inspired by the idea of grafting in botany, a hybrid algorithm called HIGAPSO is proposed, which combines IGA and IPSO to take advantages of both methods. After that, a spherical array antenna using wide-band stacked patch antenna elements is selected as a synthesis example to illustrate the power of HIGAPSO in solving realistic optimization problems. Finally, HIGAPSO is used to optimize the amplitude of the element current excitation of the spherical conformal array. Experimental results show that the hybrid algorithm is superior to GAs and PSOs when applied to both the classical test function and the practical problem of conformal antenna array synthesis.