Multi-Algorithm Joint Optimization for 2.4GHz Half-Wave Dipole Antenna Design and Performance Breakthrough
Joint optimization using NLSP and genetic algorithm for 2.4GHz antenna design. Multi-stage coarse-fine tuning achieved return loss of -65.46dB.
Optimized Design of 2.4GHz Half-Wave Dipole Antenna
Breakthrough of Numerical Optimization in Antenna Design
Antenna design is a classic and complex problem in electromagnetic engineering, involving complex trade-offs between numerous design parameters and performance metrics. This project employed advanced optimization methods to precisely tune key structural parameters (antenna arm length, conductor radius, feed gap width) to achieve preset performance targets: center frequency 2.4GHz, bandwidth greater than 100MHz, gain greater than 2dBi, and return loss (S11) less than -10dB.
The core innovation of the project lies in the joint approach using two complementary optimization techniques. Nonlinear Sequential Programming (NLSP) is a gradient-based deterministic optimization method capable of fast convergence and precise fine-tuning. In contrast, Genetic Algorithm (GA) is a population evolution-based stochastic optimization method capable of global search to avoid local optima. By linking these two methods with High Frequency Structure Simulator (HFSS), the project implemented a multi-stage coarse and fine tuning process.
The final optimized antenna parameters (arm length 60mm, conductor radius 0.633mm, feed gap 0.28mm) significantly improved performance. The key performance metric is return loss—the final design achieved -65.46 dB return loss at 2.41GHz, far superior to the mid-term design's -47.36 dB, validating the effectiveness of the multi-level optimization strategy. The final optimized antenna, with its excellent performance and compact structure, is particularly suitable for integration into smart home, wearable devices, and IoT systems supporting Wi-Fi, Bluetooth, and ZigBee protocols.