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1	Py-PSO-Circles Python program, Implementing Particle Swarm Optimization (PSO) with PySwarm
2	Py-PSO-3D Python program, Particle Swarm Optimization (PSO) to optimize the Rastrigin function

PSO
Machine Learning

Py-PSO-Circles Python program, Implementing Particle Swarm Optimization (PSO) with PySwarm

Py-PSO-Circles Python program, Implementing Particle Swarm Optimization (PSO) with PySwarm for 2D P;ot
1- Info ...
2- PSO-Particle Swarm Optimization, for 2d Plot

Create a new Python project In Visual Studio 2022

the Microsoft Visual Studio (... or 2019 or 2022) is a powerful IDE for Python language
Open/Run Microsoft Visual Studio 2022

To view Python templates, search for python.

Select the Python Application template, and select Next.

Create a new Python project In Visual Studio 2022
        On the Configure your new project screen -   (specify a name and file location for the project, and then select Create)
        Project name: Py-PSO-Circles
        Location: C:\Users\...\source\repos    (default location for Visual Studio 2022)

The new project opens in Visual Studio 2022 - (Visual Studio 2022 Compiler - IDE, to compile Python project / file )

The Visual Studio Solution Explorer window shows the project structure

Python Project Properieties - Py-PSO-Circles

Projct Folder: C:\Users\...\source\repos\Py-PSO-Circles
Startup File: Py_PSO_Circles.py
Project file: Py-PSO-Circles.sln

Download Python Project : Py-PSO-Circles.zip - (35.5 KB zip file) Download
Project consist of:
One Python Form - (Py_PSO_Circles.py )

Download Python Project : Py-PSO-Circles.zip - (35.5 KB zip file) Download

Install numpy, matplotib and pyswarms modules in Python on Windows Operating System, Using Command Prompt

1- Install numpy in Python on Windows Operating System, Using Command Prompt

pip install numpy

2- Install matplotib module in Python on Windows Operating System, Using Command Prompt

pip install matplotlib

3- Install pyswarms module in Python on Windows Operating System, Using Command Prompt

pip Install pyswarms

Source Code of Python Project - Py-PSO-Circles :

Download this Source Code at python file: Py_PSO_Circles.py - (1.29 KB Python file) download

Continue

Py-PSO-Circles Python program, Implementing PSO with PySwarm - Particle Swarm Optimization - (python file :Py_PSO_Circles.py)

#Types of Particle Swarm Optimization:

#1-Gradient Particle Swarm Optimization

#2-Hybrid Particle Swarm Optimization

#Implementing PSO with PySwarm,

#PySwarms is a python based tool that helps with swarm optimization.

#PySwarms is the best tool to integrate swarm optimization with basic optimization.

#pip install pyswarms

import numpy as np

import pyswarms as ps

from pyswarms.utils.functions import single_obj as fx

import matplotlib.pyplot as plt

from pyswarms.utils.plotters.formatters import Designer

from pyswarms.utils.plotters.formatters import Mesher

from pyswarms.utils.plotters import plot_contour, plot_surface

# Set-up all the hyperparameters

options = {'c1': 0.5, 'c2': 0.3, 'w':0.9}

# Call an instance of PSO

optimizer = ps.single.GlobalBestPSO(n_particles=10, dimensions=2, options=options)

# Perform the optimization

cost, pos = optimizer.optimize(fx.sphere, iters=50)

print("")

print ("optimizer.optimize(fx.sphere, iters=50) = ", cost, pos)

# Visualizing the function, Code for 2D plot - circles

m = Mesher(func=fx.sphere)

print("")

print ("Mesher(func=fx.sphere) = ", m)

animation = plot_contour(pos_history=optimizer.pos_history, mesher=m, mark=(0,0))

plt.figure(1)

plt.title('PSO-Particle Swarm Optimization, 2d Plot', fontsize = 10)

plt.show()

Debug of Py-PSO-Circles project, 0 Errors

Output of Py-PSO-Circles project :

1- Info

2- PSO-Particle Swarm Optimization, 2d Plot

Py-PSO-3D Python program, Particle Swarm Optimization (PSO) to optimize the Rastrigin function

Info, the solution and fitness value
Particle Swarm Optimization (PSO) to optimize the Rastrigin function

Create a new Python project In Visual Studio 2022

the Microsoft Visual Studio (... or 2019 or 2022) is a powerful IDE for Python language
Open/Run Microsoft Visual Studio 2022

To view Python templates, search for python.

Select the Python Application template, and select Next.

Create a new Python project In Visual Studio 2022
        On the Configure your new project screen -   (specify a name and file location for the project, and then select Create)
        Project name: Py-PSO-3D
        Location: C:\Users\...\source\repos    (default location for Visual Studio 2022)

The new project opens in Visual Studio 2022 - (Visual Studio 2022 Compiler - IDE, to compile Python project / file )

The Visual Studio Solution Explorer window shows the project structure

Python Project Properieties - Py-PSO-3D

Projct Folder: C:\Users\...\source\repos\Py-PSO-3D
Startup File: Py_PSO_3D.py
Project file: Py-PSO-3D.sln

Download Python Project : Py-PSO-3D.zip - (14.7 KB zip file) Download
Project consist of:
One Python Form - (Py_PSO_3D.py )

Download Python Project : Py-PSO-3D.zip - (14.7 KB zip file) Download

Install numpy and matplotib modules in Python on Windows Operating System, Using Command Prompt

1- Install numpy in Python on Windows Operating System, Using Command Prompt

pip install numpy

2- Install matplotib module in Python on Windows Operating System, Using Command Prompt

pip install matplotlib

Source Code of Python Project - Py-PSO-3D :

Download this Source Code at python file: Py_PSO_3D.py - (2.95 KB Python file) download

Continue

Py-PSO-3D Python program, Particle Swarm Optimization (PSO) to optimize the Rastrigin function - (python file :Py_PSO_3D.py)

#Particle Swarm Optimization (PSO) to optimize the Rastrigin function

import numpy as np

import matplotlib.pyplot as plt

from mpl_toolkits.mplot3d import Axes3D

# Define the Rastrigin function

def rastrigin(x):

n = len(x)

return 10*n + sum([xi**2 - 10*np.cos(2*np.pi*xi) for xi in x])

# Define the PSO algorithm

def pso(cost_func, dim=2, num_particles=30, max_iter=100, w=0.5, c1=1, c2=2):

# Initialize particles and velocities

particles = np.random.uniform(-5.12, 5.12, (num_particles, dim))

velocities = np.zeros((num_particles, dim))

# Initialize the best positions and fitness values

best_positions = np.copy(particles)

best_fitness = np.array([cost_func(p) for p in particles])

swarm_best_position = best_positions[np.argmin(best_fitness)]

swarm_best_fitness = np.min(best_fitness)

# Iterate through the specified number of iterations, updating the velocity and position of each particle at each iteration

for i in range(max_iter):

# Update velocities

r1 = np.random.uniform(0, 1, (num_particles, dim))

r2 = np.random.uniform(0, 1, (num_particles, dim))

velocities = w * velocities + c1 * r1 * (best_positions - particles) + c2 * r2 * (swarm_best_position - particles)

# Update positions

particles += velocities

# Evaluate fitness of each particle

fitness_values = np.array([cost_func(p) for p in particles])

# Update best positions and fitness values

improved_indices = np.where(fitness_values < best_fitness)

best_positions[improved_indices] = particles[improved_indices]

best_fitness[improved_indices] = fitness_values[improved_indices]

if np.min(fitness_values) < swarm_best_fitness:

swarm_best_position = particles[np.argmin(fitness_values)]

swarm_best_fitness = np.min(fitness_values)

# Return the best solution found by the PSO algorithm

return swarm_best_position, swarm_best_fitness

# Define the dimensions of the problem

dim = 3

# Run the PSO algorithm on the Rastrigin function

solution, fitness = pso(rastrigin, dim=dim)

print("")

print ("Rastrigin function = 10*n + sum([xi**2 - 10*np.cos(2*np.pi*xi) : ", rastrigin)

# Print the solution and fitness value

print('Solution value :', solution)

print("")

print('Fitness value :', fitness)

print("")

# Create a meshgrid for visualization

x = np.linspace(-5.12, 5.12, 100)

y = np.linspace(-5.12, 5.12, 100)

X, Y = np.meshgrid(x, y)

Z = rastrigin([X, Y])

# Create a 3D plot of the Rastrigin function

fig = plt.figure(1)

ax = fig.add_subplot(111, projection='3d')

ax.plot_surface(X, Y, Z, cmap='viridis')

ax.set_xlabel('x')

ax.set_ylabel('y')

ax.set_zlabel('z')

plt.title('Particle Swarm Optimization (PSO) to optimize the Rastrigin function', fontsize = 10)

# Plot the solution found by the PSO algorithm

ax.scatter(solution[0], solution[1], fitness, color='red')

plt.show()

Debug of Py-PSO-3D project, 0 Errors

Output of Py-PSO-3D project, Particle Swarm Optimization (PSO) to optimize the Rastrigin function:

1- Info

2- Particle Swarm Optimization (PSO) to optimize the Rastrigin function

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