def simulate(self):
all_vals = []
avg_vals = []
pnt = get_best_point(self.population.points)
all_vals.append(pnt.z)
avg_vals.append(self.population.get_average_objective())
print("Initial best value: " + str(pnt.z))
while self.iteration < self.num_iterations:
if self.print_status == True and self.iteration%50 == 0:
pnt = get_best_point(self.population.points)
print pnt.z, self.population.get_average_objective()
self.iterate()
all_vals.append(get_best_point(self.population.points).z)
avg_vals.append(self.population.get_average_objective())
if self.visualize == True and self.iteration%2==0:
self.population.get_visualization()
# sns.figure(0)
plt.plot(all_vals, 'r', label='Best')
plt.plot(avg_vals, 'g', label='Average')
plt.legend()
plt.xlabel('Iterations')
plt.ylabel('Objective Function Value')
plt.title(self.func.func_name + ', ' + str(self.population.dim) + '-D')
plt.show()
pnt = get_best_point(self.population.points)
print("Final best value: " + str(pnt.z))
return pnt.z
def simulate(self):
pnt = get_best_point(self.population.points)
print("Initial best value: " + str(pnt.z))
while self.iteration < self.num_iterations:
self.iterate()
pnt = get_best_point(self.population.points)
print("Final best value: " + str(pnt.z))
return pnt.z
def simulate(self):
pnt = get_best_point(self.population.points)
print("Initial best value: " + str(pnt.z))
while self.iteration < self.num_iterations:
self.iterate()
pnt = get_best_point(self.population.points)
print("Final best value: " + str(pnt.z))
return pnt.z
def simulate(self):
pnt = get_best_point(self.population.points)
# print('best value of: ' + str(pnt.z) + ' at ' + str(pnt.coords)
print('Initial best value of: ' + str(pnt.z))
while self.iteration < self.numIterations:
self.iterate()
pnt = get_best_point(self.population.points)
# print('best value of: ' + str(pnt.z) + ' at ' + str(pnt.coords)
print('Final best value of: ' + str(pnt.z))
print("")
return pnt.z
def simulate(self):
pnt = get_best_point(self.population.points)
# print('best value of: ' + str(pnt.z) + ' at ' + str(pnt.coords)
print('Initial best value of: ' + str(pnt.z))
while self.iteration < self.numIterations:
self.iterate()
pnt = get_best_point(self.population.points)
# print('best value of: ' + str(pnt.z) + ' at ' + str(pnt.coords)
print('Final best value of: ' + str(pnt.z))
print("")
return pnt.z
def func_cluster(self):
self.sol = []
self.C = []
C = []
for j in range(0, int(self.population.num_points / self.M)):
dist = []
pnt = get_best_point(self.population.points)
self.sol.append(pnt)
for pt in self.population.points:
p_dist = calc_dist(pt, pnt)
dist.append([pt, p_dist])
dist.sort(key=lambda i: i[1])
if len(dist) > self.M:
for i in range(0, self.M):
pt = dist[i][0]
self.C.append(pt.coords)
C.append(pt)
else:
for i in range(0, len(dist)):
pt = dist[i][0]
self.C.append(pt.coords)
C.append(pt)
self.population.remove(C)
self.jDE()
self.iteration += 1
def jDE(self):
F_old = self.F
CR_old = self.CR
#IF = random.random() < self.tau1;
#ICR = random.random() < self.tau2;
F_new = (
0.1 +
0.9 * random.random()) if random.random() < self.tau1 else self.F
CR_new = (
0.0 +
1.0 * random.random()) if random.random() < self.tau2 else self.CR
if (F_old == F_new) or (CR_old == CR_new):
self.iterate()
else:
self.iterate()
pnt = get_best_point(self.population.points)
self.F = F_new
self.CR = CR_new
self.iterate()
pnt_1 = get_best_point(self.population.points)
if pnt_1.z >= pnt.z:
self.F = F_old
self.CR = CR_old
self.iterate()
def iterate(self):
leader = self.get_leader()
# print(leader.z)
for ix in range(len(self.population.points)):
curr_point = self.population.points[ix]
path_value = 0
p_vec = self.generate_perturbation()
new_points = []
while path_value <= self.pathLength:
new_point = Point(dim=self.dim)
for dx in range(new_point.dim):
new_point.coords[dx] = curr_point.coords[dx] + (leader.coords[dx] - curr_point.coords[
dx]) * path_value * p_vec[dx]
new_point.evaluate_point()
new_points.append(new_point)
path_value += self.step
self.population.points[ix] = get_best_point(new_points)
self.iteration += 1
def iterate(self):
leader = self.get_leader()
# print(leader.z)
for ix in range(len(self.population.points)):
curr_point = self.population.points[ix]
path_value = 0
p_vec = self.generate_perturbation()
new_points = []
while path_value <= self.pathLength:
new_point = Point(dim=self.dim)
for dx in range(new_point.dim):
new_point.coords[dx] = curr_point.coords[dx] + (
leader.coords[dx] -
curr_point.coords[dx]) * path_value * p_vec[dx]
new_point.evaluate_point()
new_points.append(new_point)
path_value += self.step
self.population.points[ix] = get_best_point(new_points)
self.iteration += 1
def simulate(self, plot=True):
all_vals = []
avg_vals = []
solutions = []
#pts = []
sol_df = pd.DataFrame(columns=[
'x1', 'x2', 'x3', 'x4', 'x5', 'x6', 'x7', 'x8', 'x9', 'x10', 'x11',
'x12', 'x13', 'x14', 'x15', 'x16', 'x17', 'x18', 'x19', 'x20',
'x21', 'x22', 'x23', 'x24', 'x25', 'x26', 'x27', 'x28', 'x29',
'x30', 'x31', 'x32', 'x33', 'x34', 'x35', 'x36', 'x37', 'x38',
'x39', 'x40', 'x41', 'x42', 'x43', 'x44', 'x45', 'x46', 'x47',
'x48', 'x49', 'x50', 'cluster', 'gen', 'opp'
])
pnt = get_best_point(self.population.points)
all_vals.append(pnt.z)
avg_vals.append(self.population.get_average_objective())
print("Initial best value: " + str(pnt.z))
if plot == True:
print("Initial pop: ")
self.population.get_visualization(plot_leaders=True)
while self.iteration < self.num_iterations:
self.func_cluster()
solu = []
opp = []
for pt in self.sol:
solu.append(pt.coords.tolist())
opp.append(pt.z)
sol_temp = pd.DataFrame(
solu,
columns=[
'x1', 'x2', 'x3', 'x4', 'x5', 'x6', 'x7', 'x8', 'x9',
'x10', 'x11', 'x12', 'x13', 'x14', 'x15', 'x16', 'x17',
'x18', 'x19', 'x20', 'x21', 'x22', 'x23', 'x24', 'x25',
'x26', 'x27', 'x28', 'x29', 'x30', 'x31', 'x32', 'x33',
'x34', 'x35', 'x36', 'x37', 'x38', 'x39', 'x40', 'x41',
'x42', 'x43', 'x44', 'x45', 'x46', 'x47', 'x48', 'x49',
'x50'
])
sol_temp['cluster'] = [i for i in range(0, len(solu))]
sol_temp['gen'] = [self.iteration for i in sol_temp.x1]
sol_temp['opp'] = np.array(opp)
sol_df = sol_df.append(sol_temp)
self.population.prepare(self.C)
if self.print_status == True and self.iteration % 100 == 0:
pnt = get_best_point(self.population.points)
print(pnt.z, self.population.get_average_objective())
best = get_best_point(self.population.points)
solutions.append(best.coords)
#pts.append(best)
all_vals.append(best.z)
avg_vals.append(self.population.get_average_objective())
# sns.figure(0)
if plot == True:
print('end pop:')
self.population.get_visualization(plot_leaders=True)
self.plot_contour_sol(solutions)
plt.plot(all_vals, 'r', label='Best')
plt.plot(avg_vals, 'g', label='Average')
plt.legend()
plt.xlabel('Iterations')
plt.ylabel('Objective Function Value')
plt.title(self.func.func_name + ', ' + str(self.population.dim) +
'-D')
plt.show()
pnt = get_best_point(self.population.points)
print("Final best value: " + str(pnt.z))
return sol_df, pnt.z
