Design of a Broadband Electrical Impedance Matching Network for Piezoelectric Ultrasound Transducers Based on a Genetic Algorithm

Abstract

An improved method based on a genetic algorithm (GA) is developed to design a broadband electrical impedance matching network for piezoelectric ultrasound transducer. A key feature of the new method is that it can optimize both the topology of the matching network and perform optimization on the components. The main idea of this method is to find the optimal matching network in a set of candidate topologies. Some successful experiences of classical algorithms are absorbed to limit the size of the set of candidate topologies and greatly simplify the calculation process. Both binary-coded GA and real-coded GA are used for topology optimization and components optimization, respectively. Some calculation strategies, such as elitist strategy and clearing niche method, are adopted to make sure that the algorithm can converge to the global optimal result. Simulation and experimental results prove that matching networks with better performance might be achieved by this improved method.

Optimal Pattern Synthesis of Linear Array and Broadband Design of Whip Antenna Using Grasshopper Optimization Algorithm

“…e selection of component parameters for each loading point branch will be determined by GOA. e objective function of optimization calculation is to minimize the maximum VSWR and maximize the minimum gain of each sampling frequency point in the band [33,34,45].…”

“…Many swarm intelligent algorithms have been successfully applied to antenna array pattern synthesis or antenna broadband optimization, such as genetic algorithm (GA) [6,7], ant colony optimization (ACO) [8,9], particle swarm optimization (PSO) [10][11][12], invasive weed optimization (IWO) [13], cat swarm optimization (CSO) [14], spider monkey optimization (SMO) [15], butterfly mating optimization (BMO) [16,17], social group optimization (SGO) [18], grey wolf optimization (GWO) [19], quadratic programming method (QPM) [20], flower pollination algorithm (FPA) [21], ant lion optimization (ALO) [22], firefly algorithm (FA) [23][24][25], cuckoo search (CS) [26,27], chaotic adaptive butterfly mating optimization (CABMO) [28], modified spider monkey optimization (MSMO) [29], enhanced firefly algorithm (EFA) [30], bat flower pollination (BFP) algorithm [31], gravitational search algorithm (GSA) [32], and so on. For antenna broadband optimization, there are also GA [33,34], evolutionary algorithm (EA) [35], real frequency technology [36], IWO [37][38][39], etc. e above algorithms all show their stronger robustness and search ability to solve the electromagnetic optimization problems.…”

A Review of Electric Impedance Matching Techniques for Piezoelectric Sensors, Actuators and Transducers

“…The deviations in the component values of the EIMN and the impedance of the load and source can also be investigated. An et al [52] have proposed a genetic algorithm based design method for EIMN involving complex network to design and optimize their topology.…”

Parametric Acoustic Array and Its Application in Underwater Acoustic Engineering

“…A relatively simple and effective method is based on the Smith chart, assuming that the bandwidth has an inverse relationship with the quality factor Q [50]. Moreover, some researchers use optimization algorithms such as the genetic algorithm to search for optimal EIMN designs [51]. The receiver is divided into the primary and difference frequency receiving circuits.…”