دوره هوش مصنوعی با PICTOBLOX

				
					import numpy as np
import matplotlib.pyplot as plt

# Objective function (to optimize)
def objective_function(x, y):
    return np.sin(np.sqrt(x**2 + y**2))

# Initialize 10 particles
particles = np.random.uniform(-5, 5, (10, 2))
best_positions = particles.copy()

# Number of iterations
iterations = 20

# Store movement trajectory
trajectories = [[] for _ in range(len(particles))]

for _ in range(iterations):
    for i, (x, y) in enumerate(particles):
        # Compute objective function value
        if objective_function(x, y) > objective_function(*best_positions[i]):
            best_positions[i] = np.array([x, y])
        
        # Update particle position
        particles[i] += np.random.uniform(-0.5, 0.5, 2)
        trajectories[i].append(particles[i].copy())

# Plot
x_vals = np.linspace(-5, 5, 100)
y_vals = np.linspace(-5, 5, 100)
X, Y = np.meshgrid(x_vals, y_vals)
Z = objective_function(X, Y)

plt.figure(figsize=(6, 4))
plt.contourf(X, Y, Z, levels=20, cmap="viridis")
for traj in trajectories:
    traj = np.array(traj)
    plt.plot(traj[:, 0], traj[:, 1], "r-", alpha=0.7)  # Movement path
plt.scatter(best_positions[:, 0], best_positions[:, 1], color='green', label="Best positions")
plt.title("Optimization with PSO")
plt.xlabel("X")
plt.ylabel("Y")
plt.legend()
plt.show()