Sparse Synthesis of Concentric Circular Antenna Array via Multi-Objective Evolutionary Computation

Abstract

The sparse synthesis of the concentric circular antenna array (CCAA) is a very important technology because it is able to reduce the cost of the antenna array. In this paper, we first formulate a multi-objective optimization problem to jointly reduce the maximum sidelobe level (SLL) and the number of the switched-on elements of the CCAA. Then, we propose a novel enhanced non-dominated sorting genetic algorithm-II (ENSGA-II) to solve this problem. ENSGA-II introduces a hierarchy mechanism to improve the population utilization of the conventional non-dominated sorting genetic algorithm, thereby enhancing the accuracy and the convergence rate of the algorithm. Simulation results show that ENSGA-II obtains a lower maximum SLL with the similar number the switched-off elements compared with other algorithms. Moreover, ENSGA-II has a faster convergence rate.