A collection of nature inspired optimization algorithms. Every optimization algorithm inherits from the BaseOptimizer class using the step() function from each optimization algorithm.
Implemented so far:
- Artificial Bee Colony
- Cuckoo Search
- Bat Search
- Firefly Search
- Whale Optimization Algorithm
- Gray Wolf Optimizer
Setup venv:
make setup
Install requirements:
make install
Run an example script:
make run
Test:
make test
import numpy as np
from population import Population
from artifical_bee_colony import ArtificialBeeColony
from example_functions import sphere
population_size = 25
dim_individual = 2
lb = -5.12
ub = 5.12
error_tol = 0.01
limit = 100
n_generations = 100
objective_function = sphere
# Generate a population
population = Population(population_size,
dim_individual,
lb,
ub,
objective_function
)
# Artificial Bee Colony Algorithm
abc = ArtificialBeeColony(population,
limit,
n_generations,
error_tol=error_tol,
verbose=False
)
result = abc.run()
print("Artificial Bee Colony Algorithm")
print(f"Best solution: {result.best_solution}")
print(f"Best solution fitness: {result.best_fitness:.2f}")
result.plot_phenotypic_diversity()
result.plot_genotypic_diversity()- More algorithms:
-
Dragonfly
-
Flower Pollination Algorithm
-
Simulated Annealing
-
Particle Swarm Obtimization
-
Genetic Algorithm
-