Nature Inspired Optimization Algorithms
Project description
NIA
Nature Inspired Optimization Algorithms
Instalation
Check NIA's PyPI page or simply install it using pip:
pip install nia
Usage
Solve Ackley problem using Genetic Algorithm:
from nia.algorithms import GeneticAlgorithm
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
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:
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 :
- Genetic algorithm (GeneticAlgorithm)
- Differential Evolution
- Evolutionary Programming
- Artificial Immune System
- Clonal Selection Algorithm
- Biogeography-based
- Symbiotic Organisms Search
- Ant Colony Optimization
- 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
- Particle Swarm Optimization (ParticleSwarmOptimization)
- Simulated Annealing Algorithm
- Gravitational Search Algorithm
- Big Bang - Big Crunch
Supported Selection Operators :
- Rank (Rank)
- Tournament (Tournament)
Supported Cross Over Operators :
- K-Point (KPoint)
- SBX (SBX)
- Random SBX (RandomSBX)
Supported Mutation Operators :
- Uniform (Uniform)
Project details
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