pyMetaheuristic

Due to pyMetaheuristic is not able to fork. This repo is created to support that repo.

Introduction

A python library for the following Metaheuristics:

Adaptive Random Search, Ant Lion Optimizer, Arithmetic Optimization Algorithm, Artificial Bee Colony Optimization, Artificial Fish Swarm Algorithm, Bat Algorithm, Biogeography Based Optimization, Cross-Entropy Method, Crow Search Algorithm, Cuckoo Search, Differential Evolution, Dispersive Flies Optimization, Dragonfly Algorithm, Firefly Algorithm, Flow Direction Algorithm, Flower Pollination Algorithm, Genetic Algorithm, Grasshopper Optimization Algorithm, Gravitational Search Algorithm, Grey Wolf Optimizer, Harris Hawks Optimization, Improved Grey Wolf Optimizer, Improved Whale Optimization Algorithm, Jaya, Jellyfish Search Optimizer, Krill Herd Algorithm, Memetic Algorithm, Moth Flame Optimization, Multiverse Optimizer, Pathfinder Algorithm, Particle Swarm Optimization, Random Search, Salp Swarm Algorithm, Simulated Annealing, Sine Cosine Algorithm, Student Psychology Based Optimization; Symbiotic Organisms Search; Teaching Learning Based Optimization, Whale Optimization Algorithm.

Usage

1.Install

pip install pyMetaheuristic

2.Import

# Import PSO
from pyMetaheuristic.algorithm import particle_swarm_optimization

# Import a Test Function. Available Test Functions: https://bit.ly/3KyluPp
from pyMetaheuristic.test_function import easom

# OR Define your Own Custom Function. The function input should be a list of values, 
# each value represents a dimenstion (x1, x2, ...xn) of the problem.
import numpy as np
def easom(variables_values = [0, 0]):
    x1, x2     = variables_values
    func_value = -np.cos(x1)*np.cos(x2)*np.exp(-(x1 - np.pi)**2 - (x2 - np.pi)**2)
    return func_value

# Run PSO
parameters = {
    'swarm_size': 250,
    'min_values': (-5, -5),
    'max_values': (5, 5),
    'iterations': 500,
    'decay': 0,
    'w': 0.9,
    'c1': 2,
    'c2': 2
}
pso = particle_swarm_optimization(target_function = easom, **parameters)

# Print Solution
variables = pso[:-1]
minimum   = pso[ -1]
print('Variables: ', np.around(variables, 4) , ' Minimum Value Found: ', round(minimum, 4) )

# Plot Solution
from pyMetaheuristic.utils import graphs
plot_parameters = {
    'min_values': (-5, -5),
    'max_values': (5, 5),
    'step': (0.1, 0.1),
    'solution': [variables],
    'proj_view': '3D',
    'view': 'browser'
}
graphs.plot_single_function(target_function = easom, **plot_parameters)

3.Colab Demo

Try it in Colab:

Algorithm Name	Colab
Adaptive Random Search
Ant Lion Optimizer
Arithmetic Optimization Algorithm
Artificial Bee Colony Optimization
Artificial Fish Swarm Algorithm
Bat Algorithm
Biogeography Based Optimization
Cross-Entropy Method
Crow Search Algorithm
Cuckoo Search
Differential Evolution
Dispersive Flies Optimization
Dragonfly Algorithm
Firefly Algorithm
Flow Direction Algorithm
Flower Pollination Algorithm
Genetic Algorithm
Grey Wolf Optimizer
Grasshopper Optimization Algorithm
Gravitational Search Algorithm
Harris Hawks Optimization
Improved Grey Wolf Optimizer
Improved Whale Optimization Algorithm
Jaya
Jellyfish Search Optimizer
Krill Herd Algorithm
Memetic Algorithm
Moth Flame Optimization
Multiverse Optimizer
Pathfinder Algorithm
Particle Swarm Optimization
Random Search
Salp Swarm Algorithm
Simulated Annealing
Sine Cosine Algorithm
Student Psychology Based Optimization
Symbiotic Organisms Search
Teaching Learning Based Optimization
Whale Optimization Algorithm

4. Test Functions

• Available Test Functions: https://bit.ly/3KyluPp
• Test Functions and their Optimal Solutions with 2D or 3D plots

Multiobjective Optimization or Many Objectives Optimization

For Multiobjective Optimization or Many Objectives Optimization try pyMultiobjective

TSP (Travelling Salesman Problem)

For Travelling Salesman Problems try pyCombinatorial

Acknowledgement

This section is dedicated to all the people that helped to improve or correct the code. Thank you very much!

• Raiser (01.MARCH.2022) - https://github.com/mpraiser - University of Chinese Academy of Sciences (China)