Chaos Fusion Mutation-Based Weighted Mean of Vectors Algorithm for Linear Antenna Array Optimization
文献类型：期刊论文
| 作者 | Chen, Zhuo2; Liu, Yan2; Dong L(董亮)1; Liu, Anyong2; Wang, Yibo2 |
| 刊名 | Sensors
 |
| 出版日期 | 2025-10 |
| 卷号 | 25期号:20 |
| 关键词 | linear antenna arrays pattern synthesis sidelobe suppression null steering metaheuristic optimization |
| DOI | 10.3390/s25206482 |
| 产权排序 | 第2完成单位 |
| 文献子类 | Journal article (JA) - |
| 英文摘要 | Highlights: What are the main findings? CFMINFO is a weighted-mean optimizer with good-lattice initialization, STC chaos, and cloud mutation, designed for constrained array synthesis. It optimizes both element spacings and amplitudes, ensuring prescribed deep-null steering. What is the implication of the main finding? CFMINFO achieves SLL ≈ −32.30 dB and a −125.1 dB deep null at 104°, while preserving the main lobe for effective interference suppression. It outperforms PSO/GA/IWO/HSA/FPA, demonstrating the best Friedman rank ≈ 1.36 on 7 CEC2020 constrained optimization tasks. This study proposes the Chaos Fusion Mutation-Based Weighted Mean of Vectors Algorithm, an advanced optimization technique within the weighted mean of vectors (INFO) framework for synthesizing unequally spaced linear arrays. The proposed algorithm incorporates three complementary mechanisms: a good-point-set initialization to enhance early population coverage, a sine–tent–cosine (STC) chaos–based adaptive parameterization to balance exploration and exploitation, and a normal-cloud mutation to preserve diversity and prevent premature convergence. Array-factor (AF) optimization is posed as a constrained problem, simultaneously minimizing sidelobe level (SLL) and achieving deep-null steering, with penalties applied to enforce geometric and engineering constraints. Across diverse array-synthesis tasks, the proposed algorithm consistently attains lower peak SLLs and more accurate nulls, with faster and more stable convergence than benchmark metaheuristics. Across five simulation scenarios, it demonstrates robust superiority, notably surpassing an enhanced IWO in the combined objectives of deep-null suppression and maximum SLL reduction. In a representative engineering example, we obtain an SLL and a deep null of approximately −32.30 and −125.1 dB, respectively, at 104°. Evaluation of the CEC2020 real-world constrained problems confirms robust convergence and competitive statistical ranking. For reproducibility, all data and code are publicly accessible, as detailed in the Data Availability section. © 2025 by the authors. |
| 学科主题 | 电子、通信与自动控制技术 |
| URL标识 | 查看原文 |
| 资助项目 | N/A |
| 语种 | 英语 |
| WOS记录号 | WOS:001602772300001 |
| 资助机构 | N/A |
| 源URL | [http://ir.ynao.ac.cn/handle/114a53/28700]  |
| 专题 | 云南天文台_射电天文研究组 |
| 作者单位 | 1.Yunnan Province China-Malaysia HF-VHF Advance Radio, Astronomy Technology International Joint Laboratory, Yunnan Observatory, Chinese Academy of Sciences, Kunming, 650011, China 2.School of Physics and Electronic Information, Yunnan Normal University, Kunming, 650504, China; |
| 推荐引用方式 GB/T 7714 | Chen, Zhuo,Liu, Yan,Dong L,et al. Chaos Fusion Mutation-Based Weighted Mean of Vectors Algorithm for Linear Antenna Array Optimization[J]. Sensors,2025,25(20). |
| APA | Chen, Zhuo,Liu, Yan,董亮,Liu, Anyong,&Wang, Yibo.(2025).Chaos Fusion Mutation-Based Weighted Mean of Vectors Algorithm for Linear Antenna Array Optimization.Sensors,25(20). |
| MLA | Chen, Zhuo,et al."Chaos Fusion Mutation-Based Weighted Mean of Vectors Algorithm for Linear Antenna Array Optimization".Sensors 25.20(2025). |
入库方式： OAI收割
来源：云南天文台
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