https://github.com/salar-shdk/nia
Nature Inspired Optimization Algorithms
https://github.com/salar-shdk/nia
algorithm ant-colony-optimization artificial-bee-colony bioinspired cuckoo-search differential-evolution evolutionary-algorithms evolutionary-programming genetic-algorithm grey-wolf-optimizer huristic nature-inspired-algorithms nature-inspired-computation nia optimization optimization-algorithms particle-swarm-optimization python simulated-annealing-algorithm
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Nature Inspired Optimization Algorithms
- Host: GitHub
- URL: https://github.com/salar-shdk/nia
- Owner: salar-shdk
- License: mit
- Created: 2021-08-29T14:44:36.000Z (almost 5 years ago)
- Default Branch: main
- Last Pushed: 2023-02-13T18:35:06.000Z (over 3 years ago)
- Last Synced: 2026-05-26T22:31:50.049Z (15 days ago)
- Topics: algorithm, ant-colony-optimization, artificial-bee-colony, bioinspired, cuckoo-search, differential-evolution, evolutionary-algorithms, evolutionary-programming, genetic-algorithm, grey-wolf-optimizer, huristic, nature-inspired-algorithms, nature-inspired-computation, nia, optimization, optimization-algorithms, particle-swarm-optimization, python, simulated-annealing-algorithm
- Language: Python
- Homepage:
- Size: 65.4 KB
- Stars: 27
- Watchers: 4
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# NIA
NIA is a python package for Nature Inspired Optimization Algorithms which makes optimization process easy and fast.
# Instalation
Check [NIA's PyPI page](https://pypi.org/project/nia/) or simply install it using pip:
```
pip install nia
```
# Usage
Solve Ackley problem using Genetic Algorithm:
```python
from nia.algorithms import GeneticAlgorithm
from nia.problems import ackley
nia = GeneticAlgorithm(cost_function=ackley,
lower_bond=[-5,-5],
upper_bond=[5,5],
)
nia.run()
print(nia.message);
```
output:
```
quit criteria reached best answer is: [-0.02618036 -0.03615453] and best fitness is: 0.0006327163637145361 iteration : 11
```
Plot:
## Customization:
```python
from nia.algorithms import GeneticAlgorithm
# Specific selection, crossover and muttion algorithms are available under related sub-packages.
from nia.selections import Tournament
from nia.crossovers import RandomSBX
from nia.mutations import Uniform
import numpy as np
def ackley(X):
x = X[0]
y = X[1]
return -20 * np.exp(-0.2 * np.sqrt(0.5 * (x**2 + y**2))) - np.exp(0.5 *
(np.cos(2 * np.pi * x) + np.cos(2 * np.pi * y))) + np.e + 20
def log(ga):
print(ga.best)
lower = np.array([-5,-5])
upper = np.array([5,5])
nia = GeneticAlgorithm(cost_function=ackley,
iteration_function=log,
lower_bond=lower,
upper_bond=upper,
quit_criteria = 0.0001,
num_variable = 2,
num_population = 20,
max_iteration = 100,
crossover = RandomSBX(2),
mutation = Uniform(0.05),
selection = Tournament(20)
)
nia.run()
print(nia.message);
```
output
```
max iteration reached best answer so far: [-0.02618036 -0.03615453] with best fitness: 0.1786046633597529 iteration : 99
```
# Supported Algorithms :
- [x] Genetic algorithm (GeneticAlgorithm)
- [ ] Differential Evolution
- [ ] Evolutionary Programming
- [ ] Artificial Immune System
- [ ] Clonal Selection Algorithm
- [ ] Biogeography-based
- [ ] Symbiotic Organisms Search
- [ ] Ant Colony Optimization
- [x] Artificial Bee Colony (ArtificialBeeColony)
- [ ] Moth Flame Optimization Algorithm
- [ ] Cuckoo Search
- [ ] Green Herons Optimization Algorithm
- [ ] Bat Algorithm
- [ ] Whale Optimization Algorithm
- [ ] Krill Herd
- [ ] Fish-swarm Algorithm
- [ ] Grey Wolf Optimizer
- [ ] Shuffle frog-leaping Algorithm
- [ ] Cat Swarm Optimization
- [ ] Flower Pollination Algorithm
- [ ] Invasive Weed Optimization
- [ ] Water Cycle Algorithm
- [ ] Teaching–Learning-Based Optimization
- [x] Particle Swarm Optimization (ParticleSwarmOptimization)
- [ ] Simulated Annealing Algorithm
- [ ] Gravitational Search Algorithm
- [ ] Big Bang - Big Crunch
# Supported Selection Operators :
- [x] Rank (Rank)
- [x] Tournament (Tournament)
# Supported Cross Over Operators :
- [x] K-Point (KPoint)
- [x] SBX (SBX)
- [x] Random SBX (RandomSBX)
# Supported Mutation Operators :
- [x] Uniform (Uniform)