The suppression of the side-lobe level (SLL) of antenna arrays is a significant factor that can enhance the reliability and validity of a communication system. Recently, metaheuristic algorithms have been widely implemented in the design of antenna arrays, in order to find the optimal minimization for the side-lobe level of the array's radiation pattern. In this paper, we propose a new hybrid algorithm that combines the characteristics of two stochastic algorithms, Antlion Optimization (ALO) algorithm and Grasshopper Optimization Algorithm (GOA). ALO, which is an evolutionary algorithm, is robust in exploitation and has been effectively used in many articles in the literature. GOA has strong capability of exploration all over the search space due to the swarm nature of the algorithm, which has been proven in several articles in the literature. Therefore, combining these characteristics and overcoming the drawbacks of ALO and GOA are the main motivation behind hybridizing ALO and GOA in one hybrid algorithm. Simulation results show that the proposed hybrid algorithm has a good performance in the radiation pattern optimization of circular antenna array (CAA) and fast convergence rate compared with other strong optimization algorithms, which prove the efficiency, robustness, and stability of the hybrid algorithm.
Anas Atef Amaireh,
Asem Sh. Al-Zoubi,
Nihad I. Dib,
"Sidelobe-Level Suppression for Circular Antenna Array via New Hybrid Optimization Algorithm Based on Antlion and Grasshopper Optimization Algorithms," Progress In Electromagnetics Research C,
Vol. 93, 49-63, 2019. doi:10.2528/PIERC19040909
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