for i, particle in enumerate(cloud_particles, start=0):
particle.initialize_coordinates(pso.sup_limit, pso.inf_limit)
particle.neighbors = topology[i]
pso.best_neighbor(cloud_particles, particle)
# scatter_x.append(particle.x[0])
# scatter_y.append(particle.x[1])
#
# plt.plot(scatter_x, scatter_y, 'o', color='orange', label='Initial Cloud')
# plt.legend()
dict_iter = {}
vector_iter = []
while k < max_iteration:
for i, particle in enumerate(cloud_particles, start=0):
vector_iter.append(particle.x[:])
if pso.f(particle.x) < pso.f(particle.p_best):
particle.p_best = particle.x[:]
if pso.f(particle.x) < pso.f(pso.g_best):
pso.g_best = particle.x[:]
for j in range(particle.dimension):
pij = particle.p_best[j]
gj = pso.g_best[j]
xij = particle.x[j]
particle.velocity[j] = pso.calculate_velocity(pij, gj, xij)
particle.update_velocity()
dict_iter[k] = vector_iter
vector_iter = []
k += 1
best_fitness = pso.f(pso.g_best)
print(
f'f6(x): {pso.inf_limit} <= xi <= {pso.sup_limit}\n'
m = int(sys.argv[4])
iteracoes = int(sys.argv[5])
atual = 0
pso = PSO(w, c_1, c_2, [-10, 10], dimensao)
nuvemParticulas = pso.inicia_nuvem(m)
topology = pso.arruma_vizinhos(nuvemParticulas)
for i, particula in enumerate(nuvemParticulas, start=0):
particula.inicia_particula(pso.limiteSuperior, pso.limiteInferior)
particula.vizinho = topology[i]
pso.melhor_vizinho(nuvemParticulas, particula)
while atual < iteracoes:
for i, particula in enumerate(nuvemParticulas, start=0):
if pso.f(particula.posX) < pso.f(particula.melhorPessoal):
particula.melhorPessoal = particula.posX[:]
if pso.f(particula.posX) < pso.f(pso.melhorGrupo):
pso.melhorGrupo = particula.posX[:]
for j in range(particula.dimensao):
pij = particula.melhorPessoal[j]
gj = pso.melhorGrupo[j]
xij = particula.posX[j]
particula.distancia[j] = pso.distancia(pij, gj, xij)
particula.update_distancia()
atual += 1
profit = pso.f(pso.melhorGrupo)
file = 'result.csv'
fieldnames = [
'dimensao', 'qtdParticulas', 'iteracoes', 'fatorDiversidade',
