Question

Write a python code to implement optimization using Particle Swarm Optimization.

Answer 1

#program

import random
import numpy as np

W = 0.5
c1 = 0.8
c2 = 0.9

n_iterations = int(input("Inform the number of iterations: "))
target_error = float(input("Inform the target error: "))
n_particles = int(input("Inform the number of particles: "))

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("I am at ", self.position, " meu pbest is ", 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("The best solution is: ", search_space.gbest_position, " in n_iterations: ", iteration)

#output

#code