Optimal Design of Microwave Devices by Fitness-estimation-based Particle Swarm Optimization Algorithm

As important parts of modern communication systems, microwave devices play a decisive role in communication quality. When optimizing the complex microwave devices, the global optimization algorithm is generally used exploiting full-wave electromagnetic simulation software. The full-wave electromagnetic simulation software evaluates the performance of the microwave device. Based on this evaluation result, the global optimization algorithm is used to design the microwave device. This ordinary method can achieve high accuracy, but the main disadvantage is time-consuming. It takes a long time and sometimes takes days or even weeks. In order to improve the efficiency of the optimization of microwave devices, this research presents a method called fitness-estimation-based particle swarm optimization (fePSO). According to the explicit evolution formula of particle swarm optimization (PSO), the particles fitness predictive model is constructed. From the third generation, the fitness value is estimated by the predictive model, so as to replace the time-consuming full-wave electromagnetic simulation when optimizing complex microwave devices. Thereby it can greatly reduce the evaluation time of the fitness, shorten the entire optimization process, and improve the design efficiency. This method is validated by optimizing Yagi microstrip antenna (MSA) and hairpin SIR band-pass filter. The results show that the efficiency can be increased by about 90% with the assurance of design accuracy, so the purpose of rapid optimization has been achieved.