用量子粒子群算法加速天线设计，12分钟完成传统50小时工作。
Data-Driven Antenna Miniaturization: A Knowledge-Based System Integrating Quantum PSO and Predictive Machine Learning Models
- 结合量子粒子群与机器学习，自动优化天线尺寸。
- 共振频率降至1.4208GHz，比传统设计低12.7%。
- 系统在普通电脑上提速240倍，适合6G和物联网设备。
无线技术快速发展要求在有限周期内实现天线小型化与性能优化。本研究提出一种融合量子行为动态粒子群优化（QDPSO）与ANSYS HFSS仿真的机器学习增强流程，可在11.53秒内自动优化环形天线尺寸，实现1.4208 GHz的谐振频率，较传统1.60 GHz设计降低12.7%。基于936组仿真数据，支持向量机（SVM）、随机森林、XGBoost及堆叠集成模型可在0.75秒内预测谐振频率，其中堆叠模型训练精度达R²=0.9825，SVM验证性能最优（R²=0.7197）。整个设计流程（含优化、预测与验证）在标准台式机（Intel i5-8500，16GB RAM）上仅耗时12.42分钟，相较基于PSADEA的方法节省50小时，提速240倍。该系统摒弃传统试错法，实现精准性能目标设定与可制造参数自动生成，特别适用于需快速调频的紧凑型消费类设备。通过人工智能优化与CAD验证融合，显著降低工程负担并确保生产可用设计，为6G与物联网应用提供可扩展的下一代射频系统范式。
原文摘要 · Abstract (English)
The rapid evolution of wireless technologies necessitates automated design frameworks to address antenna miniaturization and performance optimization within constrained development cycles. This study demonstrates a machine learning enhanced workflow integrating Quantum-Behaved Dynamic Particle Swarm Optimization (QDPSO) with ANSYS HFSS simulations to accelerate antenna design. The QDPSO algorithm autonomously optimized loop dimensions in 11.53 seconds, achieving a resonance frequency of 1.4208 GHz a 12.7 percent reduction compared to conventional 1.60 GHz designs. Machine learning models (SVM, Random Forest, XGBoost, and Stacked ensembles) predicted resonance frequencies in 0.75 seconds using 936 simulation datasets, with stacked models showing superior training accuracy (R2=0.9825) and SVM demonstrating optimal validation performance (R2=0.7197). The complete design cycle, encompassing optimization, prediction, and ANSYS validation, required 12.42 minutes on standard desktop hardware (Intel i5-8500, 16GB RAM), contrasting sharply with the 50-hour benchmark of PSADEA-based approaches. This 240 times of acceleration eliminates traditional trial-and-error methods that often extend beyond seven expert-led days. The system enables precise specifications of performance targets with automated generation of fabrication-ready parameters, particularly benefiting compact consumer devices requiring rapid frequency tuning. By bridging AI-driven optimization with CAD validation, this framework reduces engineering workloads while ensuring production-ready designs, establishing a scalable paradigm for next-generation RF systems in 6G and IoT applications.
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