Quantum-Inspired Equilibrium Optimizer for Linear Antenna Array

With the rapid development of communication technology, the problem of antenna array optimization plays a crucial role. Among many types of antennas, line antenna arrays (LAA) are the most commonly applied, but the side lobe level (SLL) reduction is still a challenging problem. In the radiation process of the linear antenna array, the high side lobe level will interfere with the intensity of the antenna target radiation direction. Many conventional methods are ineffective in obtaining the maximum side lobe level in synthesis, and this paper proposed a quantum equilibrium optimizer (QEO) algorithm for line antenna arrays. Firstly, the linear antenna array model consists of an array element arrangement. Array factor (AF) can be expressed as the combination of array excitation amplitude and position in array space. Then, inspired by the powerful computing power of quantum computing, an improved quantum equilibrium optimizer combining quantum coding and quantum rotation gate strategy is proposed. Finally, the proposed quantum equilibrium optimizer is used to optimize the excitation amplitude of the array elements in the linear antenna array model by numerical simulation to minimize the interference of the side lobe level to the main lobe radiation. Six different metaheuristic algorithms are used to optimize the excitation amplitude in three different arrays of line antenna arrays, the experimental results indicated that the quantum equilibrium optimizer is more advantageous in obtaining the maximum side lobe level reduction. Compared with other metaheuristic optimization algorithms, the quantum equilibrium optimizer has advantages in terms of convergence speed and accuracy.

Automated summary

With the rapid development of communication technology, the reduction of side lobe level (SLL) in line antenna arrays (LAA) remains a challenging problem. This study proposes a quantum equilibrium optimizer (QEO) algorithm for LAA optimization, which combines quantum coding and quantum rotation gate strategy. The proposed algorithm is proven to be advantageous in terms of maximum SLL reduction, convergence speed, and accuracy compared to other metaheuristic optimization algorithms.