Implementing the Particle Swarm Optimization

pso.py

#!/usr/bin/python3
"""
  * AUTHOR : https://gist.github.com/IranNeto/21542660d740ac02dfce2f6aeb11ebeb
             https://github.com/IranNeto
  * WEB    : https://medium.com/analytics-vidhya/implementing-particle-swarm-optimization-pso-algorithm-in-python-9efc2eb179a6
"""
# https://code.sololearn.com/cvIleZW1gfC6/#py
import random
import numpy as np 

W = 0.5
c1 = 0.8
c2 = 0.9 

n_iterations = int(50)
target_error = float(1e-6)
n_particles = int(30)

class Particle():
    def __init__(self):
        self.position = np.array([(-1) ** (bool(random.getrandbits(1))) * random.random()*50, (-1)**(bool(random.getrandbits(1))) * random.random()*50])
        self.pbest_position = self.position
        self.pbest_value = float('inf')
        self.velocity = np.array([0,0])

    def __str__(self):
        print("Estoy en ", self.position, " y el mejor es ", self.pbest_position)
    
    def move(self):
        self.position = self.position + self.velocity


class Space():

    def __init__(self, target, target_error, n_particles):
        self.target = target
        self.target_error = target_error
        self.n_particles = n_particles
        self.particles = []
        self.gbest_value = float('inf')
        self.gbest_position = np.array([random.random()*50, random.random()*50])

    def print_particles(self):
        for particle in self.particles:
            particle.__str__()
   
    def fitness(self, particle):
        return particle.position[0] ** 2 + particle.position[1] ** 2 + 1

    def set_pbest(self):
        for particle in self.particles:
            fitness_cadidate = self.fitness(particle)
            if(particle.pbest_value > fitness_cadidate):
                particle.pbest_value = fitness_cadidate
                particle.pbest_position = particle.position
            

    def set_gbest(self):
        for particle in self.particles:
            best_fitness_cadidate = self.fitness(particle)
            if(self.gbest_value > best_fitness_cadidate):
                self.gbest_value = best_fitness_cadidate
                self.gbest_position = particle.position

    def move_particles(self):
        for particle in self.particles:
            global W
            new_velocity = (W*particle.velocity) + (c1*random.random()) * (particle.pbest_position - particle.position) + \
                            (random.random()*c2) * (self.gbest_position - particle.position)
            particle.velocity = new_velocity
            particle.move()
            

search_space = Space(1, target_error, n_particles)
particles_vector = [Particle() for _ in range(search_space.n_particles)]
search_space.particles = particles_vector
search_space.print_particles()

iteration = 0
while(iteration < n_iterations):
    search_space.set_pbest()    
    search_space.set_gbest()

    if(abs(search_space.gbest_value - search_space.target) <= search_space.target_error):
        break

    search_space.move_particles()
    iteration += 1
    
print("La mejor posicion es : ", search_space.gbest_position, " en n_iteraciones: ", iteration)