def blind_search(name_function, size_random_search):
point_list = []
generated_data_x = [
random.uniform(static_data.get_min_range(name_function),
static_data.get_max_range(name_function))
for _ in range(size_random_search)
]
generated_data_y = [
random.uniform(static_data.get_min_range(name_function),
static_data.get_max_range(name_function))
for _ in range(size_random_search)
]
tmp = func_file.return_value_function(
[generated_data_x[0], generated_data_y[0]], name_function)
cord_X = generated_data_x[0]
cord_Y = generated_data_y[0]
for i, j in zip(generated_data_x, generated_data_y):
min = func_file.return_value_function([i, j], name_function)
point_list.append([i, j, min])
if min < tmp:
tmp = min
cord_X = i
cord_Y = j
return [cord_X, cord_Y, tmp]
def blind_search_points(name_function, size_random_search):
point_list = []
generated_data_x = [
random.uniform(static_data.get_min_range(name_function),
static_data.get_max_range(name_function))
for _ in range(size_random_search)
]
generated_data_y = [
random.uniform(static_data.get_min_range(name_function),
static_data.get_max_range(name_function))
for _ in range(size_random_search)
]
tmp = func_file.return_value_function(
[generated_data_x[0], generated_data_y[0]], name_function)
cord_X = generated_data_x[0]
cord_Y = generated_data_y[0]
for i, j in zip(generated_data_x, generated_data_y):
min = func_file.return_value_function([i, j], name_function)
point_list.append([i, j, min])
if min < tmp:
tmp = min
cord_X = i
cord_Y = j
print("X: " + str(cord_X))
print("Y: " + str(cord_Y))
print("VALUE: " + str(tmp))
print("")
return point_list
def PSO(func, dim, popSize, iteration, vmax):
population = generate_first_population(func, popSize, dim)
gBest = find_best_in_list(population, func)
new_population = []
all_points = []
points = [point[0] for point in population]
all_points.append(points)
for i in range(iteration):
for j in population:
new_velocity = []
new_point = []
for x in range(dim):
# point j = [[x,y], [velocity_x, velocity_y], [pBest_x,pBest_y]]
new_velocity.append(j[1][x] + (C1 * random.uniform(0, 1) *
(j[2][x] - j[0][x])) +
(C2 * random.uniform(0, 1) *
(gBest[x] - j[0][x])))
new_point.append(j[0][x] + new_velocity[x])
for m in range(dim):
if new_velocity[m] > vmax or new_velocity[m] < -vmax:
new_velocity[m] = new_velocity[m] / 20
for i in range(dim):
if new_point[i] > static_data.get_max_range(func):
new_point[i] = random.uniform(
static_data.get_min_range(func),
static_data.get_max_range(func))
if new_point[i] < static_data.get_min_range(func):
new_point[i] = random.uniform(
static_data.get_min_range(func),
static_data.get_max_range(func))
if func_file.return_value_function(
new_point, func) < func_file.return_value_function(
j[2], func):
pBest = new_point
else:
pBest = j[2]
if func_file.return_value_function(
pBest, func) < func_file.return_value_function(
gBest, func):
gBest = pBest
print([new_point, new_velocity, pBest])
new_population.append([new_point, new_velocity, pBest])
# for graph
points = [point[2] for point in new_population]
all_points.append(points)
population = new_population
new_population = []
print(f'gBest = {gBest}')
show_3D_graph.animate_in_graph_3D(func, all_points)
def generate_first_population(func, popsize, dimension):
population = []
for i in range(0, popsize):
gen_point = list(np.random.uniform(static_data.get_min_range(func), static_data.get_max_range(func), dimension))
evaluate = func_file.return_value_function(gen_point, func)
population.append([gen_point, evaluate])
return population
def generate_first_population(func, popSize, dimension):
population = []
for i in range(0, popSize):
gen_point = list(
np.random.uniform(static_data.get_min_range(func),
static_data.get_max_range(func), dimension))
population.append(gen_point)
return population
def animate_in_graph_3D(name_function, points):
# MY IMPORT
import static_data, func_file
range_list = np.arange(static_data.get_min_range(name_function), static_data.get_max_range(name_function),
static_data.get_range(name_function))
X = [i for i in range_list for _ in range_list]
Y = list(range_list) * len(range_list)
if name_function == "ackley":
Z = [func_file.ackley([x, y]) for x, y in zip(X, Y)]
elif name_function == "levy":
Z = [func_file.levy([x, y]) for x, y in zip(X, Y)]
elif name_function == "rastrigin":
Z = [func_file.rastrigin([x, y]) for x, y in zip(X, Y)]
elif name_function == "griewank":
Z = [func_file.griewank([x, y]) for x, y in zip(X, Y)]
elif name_function == "schwefel":
Z = [func_file.schwefel([x, y]) for x, y in zip(X, Y)]
elif name_function == "zakharov":
Z = [func_file.zakharov([x, y]) for x, y in zip(X, Y)]
elif name_function == "sphere":
Z = [func_file.sphere([x, y]) for x, y in zip(X, Y)]
elif name_function == "michalewicz":
Z = [func_file.michalewicz([x, y]) for x, y in zip(X, Y)]
fig = plt.figure()
ax = fig.gca(projection='3d')
ax.plot_trisurf(X, Y, Z, cmap='inferno', edgecolor='none')
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')
def animate(i):
ax.clear()
ax.plot_trisurf(X, Y, Z, cmap='inferno', edgecolor='none')
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')
for j in range(len(points[0])):
ax.scatter(points[i][j][0], points[i][j][1])
return ax
ani = FuncAnimation(fig, animate, frames=len(points), interval=400, repeat=False)
plt.show()
def show_graph_with_searched_point_3D(name_function, point):
# MY IMPORT
import static_data, func_file
range_list = np.arange(static_data.get_min_range(name_function), static_data.get_max_range(name_function),
static_data.get_range(name_function))
X = [i for i in range_list for _ in range_list]
Y = list(range_list) * len(range_list)
if name_function == "ackley":
Z = [func_file.ackley([x, y]) for x, y in zip(X, Y)]
elif name_function == "levy":
Z = [func_file.levy([x, y]) for x, y in zip(X, Y)]
elif name_function == "rastrigin":
Z = [func_file.rastrigin([x, y]) for x, y in zip(X, Y)]
elif name_function == "griewank":
Z = [func_file.griewank([x, y]) for x, y in zip(X, Y)]
elif name_function == "schwefel":
Z = [func_file.schwefel([x, y]) for x, y in zip(X, Y)]
elif name_function == "zakharov":
Z = [func_file.zakharov([x, y]) for x, y in zip(X, Y)]
elif name_function == "sphere":
Z = [func_file.sphere([x, y]) for x, y in zip(X, Y)]
elif name_function == "michalewicz":
Z = [func_file.michalewicz([x, y]) for x, y in zip(X, Y)]
fig = plt.figure()
ax = fig.gca(projection='3d')
ax.plot_trisurf(X, Y, Z, cmap='inferno', edgecolor='none');
ax.scatter(point[0],point[1],point[2])
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')
plt.show()