genuy 0.1.5

A genetic algorithm optimization based Uda-Yagi antenna generator,

genuy

A genetic algorithm-based Uda-Yagi antenna optimizer. Uses PyGAD as the GA engine and pymininec for antenna simulation to evolve element lengths and spacings that maximise gain, front-to-back ratio, and VSWR across a specified bandwidth.

Features

• Optimizes reflector, driven element, and an arbitrary number of director elements
• Evaluates fitness across the full bandwidth (low edge, centre, high edge) to avoid narrow-band solutions
• Adaptive mutation and tournament selection for robust multimodal search
• Parallel fitness evaluation using all available CPU cores
• Exports the best solution as an MMANA-GAL .maa file

Requirements

• Python ≥ 3.14
• pymininec ≥ 1.2.0
• pygad ≥ 3.5.0

Installation

pip install genuy

Or from source:

git clone https://github.com/r2axz/genuy
cd genuy
pip install .

Usage

genuy [OPTIONS]

Key options

Antenna parameters

Option	Default	Description
-n, --num-elements	4	Number of elements (reflector+driven+directors)
-f, --frequency	145.0	Centre frequency, MHz
-b, --bandwidth	10.0	Optimization bandwidth, MHz
-r, --element-radius	3.0	Element radius, mm
-z, --reference-impedance	50+0j	Reference impedance for VSWR, Ω

GA and output parameters

Option	Default	Description
--num-generations	200	Maximum GA generations
--num-solutions	0 (auto: 10×genes)	Population size
--percent-mating	10.0	% of population selected as parents
--mutation-percent-max	40.0	Max gene mutation % (adaptive)
--mutation-percent-min	20.0	Min gene mutation % (adaptive)
--vswr-punish-early	(off)	Return penalty on first bad-VSWR frequency
--seed	(random)	Random seed; printed so runs are reproducible
--save-maa	(none)	Save best solution as MMANA .maa file
--plot-fitness	(off)	Plot fitness vs. generation after run

Run genuy --help for the full option list.

Per-element constraints

Use --constrain-length and --constrain-spacing to narrow the search space for individual elements. Elements and spacings are numbered starting from 1. Both options are repeatable.

# Fix element 1 (reflector) length range tightly, leave others free
genuy -n 5 -f 145 -b 10 --constrain-length 1 0.51 0.53

# Constrain spacing between elements 2 and 3
genuy -n 5 -f 145 -b 10 --constrain-spacing 2 0.10 0.15

# Multiple constraints
genuy -n 5 -f 145 -b 10 \
  --constrain-length 1 0.51 0.53 \
  --constrain-length 2 0.47 0.49 \
  --constrain-spacing 1 0.12 0.18

Examples

Optimize a 4-element 145 MHz antenna and save the result:

genuy -n 4 -f 145 -b 10 --save-maa antenna.maa

Optimize a 6-element antenna, plot convergence, and save:

genuy -n 6 -f 145 -b 10 --num-generations 500 --save-maa 6el.maa --plot-fitness

Reproduce a previous run using a saved seed:

genuy -n 4 -f 145 -b 10 --seed 1234567890 --save-maa antenna.maa

Fitness function

Each candidate solution is evaluated at three frequencies — lower band edge, centre, and upper band edge. The fitness score is:

$$F = \begin{cases} \dfrac{w_\text{vswr}}{\text{VSWR}\text{worst}} + w\text{gain} \cdot G_\text{worst} + w_\text{fb} \cdot \text{FB}\text{worst} & \text{if } \text{VSWR}\text{worst} < \text{threshold} \ p_\text{vswr} + w_\text{gain} \cdot G_\text{worst} + w_\text{fb} \cdot \text{FB}_\text{worst} & \text{otherwise} \end{cases}$$

Default weights: vswr_weight=100, gain_weight=3.0, fb_weight=1.0, boom_length_weight=1.0. The hard VSWR penalty (high_vswr_penalty=-100) discourages solutions that are badly mismatched anywhere in the band.

Output

After the run the best solution is printed to stdout:

Best solution :  [0.512 0.18 0.474 0.15 0.452 0.19 0.438]
Best solution fitness :  47.23
VSWR :  1.34
Impedance :  (42.1+3.2j)
Gain :  10.8
Front-to-Back Ratio :  18.4

License

MIT — see LICENSE.