https://github.com/itis-enrico-fermi/planarevolvedantenna
2-dimensional planar antenna optimized with genetic algorithms.
https://github.com/itis-enrico-fermi/planarevolvedantenna
evolutionary-algorithms evolutionary-antenna evolved-antenna genetic-algorithm nec2 optimization-algorithms planar-antenna python radiation-pattern
Last synced: 11 months ago
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2-dimensional planar antenna optimized with genetic algorithms.
- Host: GitHub
- URL: https://github.com/itis-enrico-fermi/planarevolvedantenna
- Owner: ITIS-Enrico-Fermi
- Created: 2023-05-02T17:33:51.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2023-06-14T07:55:36.000Z (almost 3 years ago)
- Last Synced: 2025-04-05T04:05:04.273Z (about 1 year ago)
- Topics: evolutionary-algorithms, evolutionary-antenna, evolved-antenna, genetic-algorithm, nec2, optimization-algorithms, planar-antenna, python, radiation-pattern
- Language: Python
- Homepage:
- Size: 1.08 MB
- Stars: 7
- Watchers: 1
- Forks: 2
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
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README
# Planar Evolved Antenna
2-dimensional planar antenna optimized with genetic algorithms.
## Objectives
The main goal of this project is simulating the evolution of a planar antenna subject to space and shape constraints, in order to maximize isotropic gain of the "north" region (toward theta = 0, standar spherical coordinates) and minimize backlobes propagation in the south region. The simulation is carried out through a genetic algorithm framed as described in the following paragraphs.
## Problem representation
### Encoding
A planar antenna can be represented as a path with both cartesian and polar coordinates. The latter is our case (see rod-based robots). Each segment of the polygonal chain is a polar coordinate (angle, distance) whose origin is the end-point of the previous segment.
### Constraints
Throughout the simulation (at any given time of it), every path must be contained inside a circle of diameter _outer\_diameter_ - specified in _config.yaml_ file - and must avoid an inner circle of diameter _inner\_diameter_, namely the hole for the onboard camera.
### Fitness (objective function)
A linear objective function: $f(min\\_gain, std\\_dev) = k1 \cdot min\\_gain + k2 \cdot std\\_dev$
### Crossover and mutation
A single cut-point crossover is done at the moment. Crossover recombines genes from the previous generation to reach _population\_size_ individuals.
Mutation is a draw without replacement of _mutation\_rate_ * _population\_size_ individuals to which a random mutation (for both angles and lengths) is applied.
## Usage
Dependencies installation:
```bash
python3 -m pip install -r requirements.txt
```
Proof-of-concept:
```bash
cd src
python3 poc.py [-p] [-go GRAPHICS_OUTDIR] [-b] [-so STATS_OUTDIR] [-bm INSTANCES]
```
Where:
- `-p` is the short option for `--plot`.
- `-go` stands for `--graphics-outdir` (output directory where a bunch of svg files will be saved). With (`-b`) or without boundaries.
- `-so` stands for `--stats-outdir`, namely the output folder for _statsXXX.mat_ files.
- `-bm` allows the user to spawn several instances of the simulation to perform a "benchmark" of the current algorithm.
Get more details with `-h` or `--help` option.
## Outcome evaluation
> Now the big question. How to interpret the simulation's results? this task can involve a vast set of knowledge. In addition, our interpretation can not only be incomplete, but also partially wrong, so take it with a grain of salt.
A well-formed algorithm should have an increasing monotonic mean fitness (or decreasing if minimizing it): at any given time, the mean/average fitness should be greater than the previous generation's mean/average fitness. Namely, the population is evolving towards better fitness thanks to evolutionary pressure.
On the other hand we have a decreasing fitness's standard deviation, due to the fact that individuals are becoming more and more similar to each other, until they become a single individual cloned N times (for std = 0).
The fitness of the best indiviual can oscillate near the mean. A smaller value can appear, for instance, if a "destructive" mutation hits this indivual.
## Credits
- Enlightening _Advanced antenna design for a NASA satellite mission_ paper ([https://digitalcommons.usu.edu/cgi/viewcontent.cgi?article=1426&context=smallsat](https://digitalcommons.usu.edu/cgi/viewcontent.cgi?article=1426&context=smallsat))
- _Learning and evolution of artificial systems_ lectures (UniMoRe)