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

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

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

4. 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.

5. Velocity Update

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