Adaptive Synthesis Using Hybrid Genetic Algorithm and Particle Swarm Optimization for Reflectionless Filter With Lumped Elements

Abstract

In this article, an adaptive synthesis based on the hybrid genetic algorithm and particle swarm optimization (HGAPSO) is proposed for reflectionless filter design with lumped capacitors, resistors, and inductors. The synthesis starts with a preset topology, where each branch of the topology represents a small passive network of lumped elements. The proposed HGAPSO is used to trim branches and obtain proper values of elements for a required filtering response. Focus on this synthesis model, the HGAPSO is embedded with local searching policies based on random coordinate and neighborhood search to improve its searching ability. Besides, a classifier-based strategy and a probabilistic method are introduced to accelerate convergence and boost iteration. Suitable topologies and component values are determined automatically by the HGAPSO to meet the specific filtering response. To predict the response accurately, the EM-simulated result of the corresponding layout and parasitic parameter models of lumped elements are considered during the fine-tuning. Based on the mechanisms mentioned above, four reflectionless bandpass filters (BPFs) are synthesized to validate the effectiveness of the proposed synthesis procedure. The fabricated filters exhibit good selectivity and low reflection coefficient in the measurement.