A tool for the design of broadband MSMN networks on transmission lines based on the comparison of optimization heuristic algorithms

We present the development and results of a Python-programmed tool for the design of broadband multiple stub impedance matching networks (MSMN) in transmission line systems, employing a suite of heuristic optimization algorithms Based Optimization (AEO); offering comparable solutions that seek to satisfy a given matching specification, taking as the work function the magnitude of the voltage reflection coefficient, in a given bandwidth, just at the input plane to the network, connected to a line loaded with a modelable arbitrary impedance. The tool uses the indicated algorithms to determine the positions and lengths of the stubs of the MSMN network. The algorithms were obtained from the Mealpy and SciPy libraries. Figures of merit were designed that allowed comparing the quality of the solutions. The results were compared with those of the Keysight ADS simulator, and 3 test examples were executed using the algorithms, yielding viable adaptation results, which are discussed.