Open Access

ARTICLE

Quantum-Inspired Equilibrium Optimizer for Linear Antenna Array

Binwen Zhu¹, Qifang Luo^1,3,*, Yongquan Zhou^1,2,3

1 College of Artificial Intelligence, Guangxi Minzu University, Nanning, 530006, China
2 Xiangsihu College of Guangxi Minzu University, Nanning, 532100, China
3 Guangxi Key Laboratories of Hybrid Computation and IC Design Analysis, Nanning, 530006, China

* Corresponding Author: Qifang Luo. Email:

(This article belongs to this Special Issue: Computational Intelligent Systems for Solving Complex Engineering Problems: Principles and Applications)

Computer Modeling in Engineering & Sciences 2023, 137(1), 385-413. https://doi.org/10.32604/cmes.2023.026097

Received 15 August 2022; Accepted 06 December 2022; Issue published 23 April 2023

Abstract

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.

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Keywords

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