Design and Analysis of Optimization Method for Ultra-Wideband PA Based on Improved MOEA/D Algorithm Using Mixed Objective Function

This article proposes a design and optimization method for Ultra-Wideband power amplifiers (PAs) using improved multiobjective evolutionary algorithm based on decomposition (MOEA/D) and mixed optimization objective function (MOOF). In order to address the insufficient optimization capabilities of conventional MOEA/D algorithm when dealing with complex Pareto fronts, the optimization algorithm is improved by using adaptive weights, neighborhoods, and global replacement. Initially, based on the sparsity of the population and an external population, the weight vectors corresponding to invalid or crowded individuals in the population are replaced. And, the neighborhoods are adaptively adjusted based on the number of iterations and the sparsity of each individual. Then, a global replacement is employed to accelerate the convergence process. Moreover, a MOOF using the load impedance, output power, efficiency and gain is conducted in the PA optimization design. To reduce the difficulty of impedance judgment, a construction method of impedance solution set based on Poisson disk sampling has been proposed and employed for optimization. For validation, an Ultra-Wideband PA operating at 100–4000 MHz (190.2% fractional bandwidth) was designed and optimized. Measured results show a drain efficiency ranging from 56.0% to 70.9% with an output power higher than 39 dBm at saturation within the whole operational frequency band.