Hierarchical Multistate Optimization Based on Bayesian Nested Improved Meta-Heuristic Algorithm for Reconfigurable Bandpass Filter

Abstract

This letter proposes a hierarchical multistate optimization (HMO) method for the microstrip reconfigurable bandpass filter (RBPF). HMO algorithm nests the inner global optimization algorithm within the outer global optimization algorithm. The Bayesian optimization (BO) algorithm is used to optimize the outer nontunable parameters, and the improved meta-heuristic algorithm is nested to optimize the tunable parameters. The proposed optimization method was applied to two microstrip RBPFs with seven fixed parameters, four sensitive tunable variables, and three tunable states. The method achieved a 42.1%–80.2% reduction in the loss function value, thereby validating its effectiveness.