def fit(self, data):
self.n_attributes = data.shape[1]
self.pso = PSO(PSOCObjectiveFunction(data, self.n_clusters, self.n_attributes),
dim=self.n_clusters * self.n_attributes,
swarm_size=self.swarm_size,
n_iter=self.n_iter,
lo_w=self.lo_w, c1=self.c1, c2=self.c2, v_max=self.v_max)
self.pso.optimize()
self.centroids = {}
raw_centroids = self.pso.gbest.pos.reshape((self.n_clusters, self.n_attributes))
self.convergence = self.pso.optimum_cost_tracking_iter
for c in range(len(raw_centroids)):
self.centroids[c] = raw_centroids[c]
self.clusters = self.get_clusters(self.centroids, data)
self.number_of_effective_clusters = 0
for c in range(len(self.centroids)):
if len(self.clusters[c]) > 0:
self.number_of_effective_clusters = self.number_of_effective_clusters + 1
class PSOC(object):
def __init__(self, n_clusters=2, swarm_size=50, n_iter=50, lo_w=0.72, up_w=0.9, c1=1.49,
c2=1.49):
self.n_clusters = n_clusters
self.swarm_size = swarm_size
self.n_iter = n_iter
self.up_w = up_w
self.lo_w = lo_w
self.c1 = c1
self.c2 = c2
self.v_max = 0.5
def fit(self, data):
self.n_attributes = data.shape[1]
self.pso = PSO(PSOCObjectiveFunction(data, self.n_clusters, self.n_attributes),
dim=self.n_clusters * self.n_attributes,
swarm_size=self.swarm_size,
n_iter=self.n_iter,
lo_w=self.lo_w, c1=self.c1, c2=self.c2, v_max=self.v_max)
self.pso.optimize()
self.centroids = {}
raw_centroids = self.pso.gbest.pos.reshape((self.n_clusters, self.n_attributes))
self.convergence = self.pso.optimum_cost_tracking_iter
for c in range(len(raw_centroids)):
self.centroids[c] = raw_centroids[c]
self.clusters = self.get_clusters(self.centroids, data)
self.number_of_effective_clusters = 0
for c in range(len(self.centroids)):
if len(self.clusters[c]) > 0:
self.number_of_effective_clusters = self.number_of_effective_clusters + 1
@staticmethod
def get_clusters(centroids, data):
clusters = {}
for c in centroids:
clusters[c] = []
for xi in data:
dist = [np.linalg.norm(xi - centroids[c]) for c in centroids]
class_ = dist.index(min(dist))
clusters[class_].append(xi)
return clusters
def main():
opt1 = PSO(dim=30, minf=-100, maxf=100, min_init=50, max_init=100, swarm_size=50, n_iter=1000, w=0.8, lb_w=0.4, w_damp=0.99, c1=2.05, c2=2.05)
opt1.optimize(ObjectiveFunction.sphere_function)
opt2 = PSO(dim=30, minf=-30, maxf=30, min_init=15, max_init=30, swarm_size=50, n_iter=1000, w=0.8, lb_w=0.4, w_damp=0.99, c1=2.05, c2=2.05)
opt2.optimize(ObjectiveFunction.rosenbrock_function)
opt3 = PSO(dim=30, minf=-5.12, maxf=5.12, min_init=2.56, max_init=5.12, swarm_size=50, n_iter=1000, w=0.8, lb_w=0.4, w_damp=0.99, c1=2.05, c2=2.05)
opt3.optimize(ObjectiveFunction.rastrigin_function)
opt4 = PSO(dim=30, minf=-100, maxf=100, min_init=50, max_init=100, swarm_size=50, n_iter=1000, w=0.8, lb_w=0.4, w_damp=0.99, c1=2.05, c2=2.05)
opt4.optimize(ObjectiveFunction.schwefel_function)
opt5 = PSO(dim=30, minf=-600, maxf=600, min_init=300, max_init=600, swarm_size=50, n_iter=1000, w=0.8, lb_w=0.4, w_damp=0.99, c1=2.05, c2=2.05)
opt5.optimize(ObjectiveFunction.griewank_function)
opt6 = PSO(dim=30, minf=-32, maxf=32, min_init=16, max_init=32, swarm_size=50, n_iter=1000, w=0.8, lb_w=0.4, w_damp=0.99, c1=2.05, c2=2.05)
opt6.optimize(ObjectiveFunction.ackley_function)
plt.subplot(231)
plt.plot([(i+1) for i in range(len(opt1.best_cost))], opt1.best_cost, c='b')
plt.legend(["Sphere"])
plt.xlabel('Iterations')
plt.ylabel('Fitness')
plt.subplot(232)
plt.plot([(i+1) for i in range(len(opt2.best_cost))], opt2.best_cost, c='r')
plt.legend(["Rosenbrock"])
plt.xlabel('Iterations')
plt.ylabel('Fitness')
plt.subplot(233)
plt.plot([(i+1) for i in range(len(opt3.best_cost))], opt3.best_cost, c='g')
plt.legend(["Rastrigin"])
plt.xlabel('Iterations')
plt.ylabel('Fitness')
plt.subplot(234)
plt.plot([(i+1) for i in range(len(opt4.best_cost))], opt4.best_cost, c='c')
plt.legend(["Schwefel"])
plt.xlabel('Iterations')
plt.ylabel('Fitness')
plt.subplot(235)
plt.plot([(i+1) for i in range(len(opt5.best_cost))], opt5.best_cost, c='m')
plt.legend(["Griewank"])
plt.xlabel('Iterations')
plt.ylabel('Fitness')
plt.subplot(236)
plt.plot([(i+1) for i in range(len(opt6.best_cost))], opt6.best_cost, c='y')
plt.legend(["Ackley"])
plt.xlabel('Iterations')
plt.ylabel('Fitness')
plt.show()
if __name__ == '__main__':
opt = dict(options)
opt['particle_num'] = 1000
opt['max_it'] = 1000
opt['bounds'] = (-100, 100)
f = open("log8.txt", "w")
num_exec = 1
time_diff_list = [0] * num_exec
val_list = [0] * num_exec
pos_list = [None] * num_exec
for i in range(num_exec):
start = time()
pos, val = PSO(optimality_criterion, opt)
time_diff = time() - start
time_diff_list[i] = time_diff
val_list[i] = val
pos_list[i] = pos
min_index = val_list.index(min(val_list))
f.write("Particle number: " + str(opt['particle_num']) + "\n")
f.write("Max iteration: " + str(opt['max_it']) + "\n")
f.write("Bounds: " + str(opt['bounds']) + "\n\n")
f.write("Best position = [\n")
for dim in pos_list[min_index]:
f.write("\t" + str(dim) + "\n")
f.write("]\n")
from optimization.ann_criterion import optimality_criterion
from optimization.PSO import PSO
if __name__ == '__main__':
a = PSO(optimality_criterion)
print(a[0], "\n", a[1])