Multi-band impedance matching using an evolutionary algorithm ECE 539 Project Presentation - Bin Yu Multi-band impedance matching using an evolutionary algorithm

Single band Impedance Transformer Performance vs. frequency <objective function> Impedance Transformer

Multi-band Impedance Transformer Equivalent Model Optimization Variables Z1, Z2, Z3, L1, L2, and L3 -> Particle

Particle Swarm Optimization Initialize particles with random position and velocity vectors. For each particle’s position (p) evaluate fitness Loop until all particles exhaust If fitness(p) better than fitness(pbest) then pbest= p Loop until max iter Set best of pBests as gBest Update particles velocity and position Stop: giving gBest, optimal solution.

Velocity Update

Optimization Results Tri-Band transformer with different Impedance Ratio <Optimization Result> Fitness vs. Iteration