Multi-objective sparse synthesis optimization of concentric circular antenna array via hybrid evolutionary computation approach

Abstract

Sparse synthesis can reduce the maximum sidelobe level (SLL) and overhead of the concentric circular antenna array (CCAA) by switching off partial array elements. However, previous works have not considered finding a balance between the number of switched-on elements of CCAA and reducing the maximum SLL, or jointly optimizing the excitation current weights of array elements to further suppress the maximum SLL. In this work, we address these challenges by formulating a hybrid multi-objective optimization problem (MOP) with discrete and continuous solutions. To solve this hybrid NP-hard problem, we consider two approaches: the two-step and one-step methods. In the two-step method, we convert the original MOP into two subproblems and propose an enhanced non-dominated sorting genetic algorithm-II (ENSGA-II) and an improved genetic algorithm with new mutation and attraction (IGAnMA) to solve these two subproblems. ENSGA-II and IGAnMA involve the hierarchy mechanism and attraction operator to enhance the solving abilities, respectively. In the one-step method, we propose an improved hybrid non-dominated sorting genetic algorithm-II (IHNSGA-II) with global search and hybrid solution update methods to solve the MOP in one step. Numerical simulation results show that the proposed methods are effective and stable for the sparse synthesis of CCAA. Additionally, electromagnetic simulation results demonstrate the validity of the proposed approaches in a more practical environment.