code gwo in python

main.py
import random

# Define the problem and objective function
def objective_function(x):
    # Your objective function implementation here
    pass

# Grey Wolf Optimizer function
def grey_wolf_optimizer(problem_size, objective_function, lb, ub, max_iterations):
    # Initialize the grey wolves population
    wolves_positions = [[random.uniform(lb, ub) for _ in range(problem_size)] for _ in range(5)]  # You can adjust the population size (5 in this case)

    # Main optimization loop
    for iteration in range(max_iterations):
        # Evaluate the fitness of each grey wolf
        fitness_values = [objective_function(wolf_position) for wolf_position in wolves_positions]

        # Identify the alpha, beta, and delta wolves
        alpha_wolf_index = fitness_values.index(min(fitness_values))
        beta_wolf_index = fitness_values.index(sorted(fitness_values)[1])
        delta_wolf_index = fitness_values.index(sorted(fitness_values)[2])

        # Update the position of each grey wolf
        for i, _ in enumerate(wolves_positions):
            for j, _ in enumerate(wolves_positions[i]):
                a = 2 * (1 - (iteration / max_iterations))  # Parameter to control the hunting behavior
                r1 = random.random()
                r2 = random.random()
                A1 = 2 * a * r1 - a
                C1 = 2 * r2
                D_alpha = abs(C1 * wolves_positions[alpha_wolf_index][j] - wolves_positions[i][j])

                r1 = random.random()
                r2 = random.random()
                A2 = 2 * a * r1 - a
                C2 = 2 * r2
                D_beta = abs(C2 * wolves_positions[beta_wolf_index][j] - wolves_positions[i][j])

                r1 = random.random()
                r2 = random.random()
                A3 = 2 * a * r1 - a
                C3 = 2 * r2
                D_delta = abs(C3 * wolves_positions[delta_wolf_index][j] - wolves_positions[i][j])

                new_position = (wolves_positions[i][j] + (A1 * D_alpha) + (A2 * D_beta) + (A3 * D_delta))
                new_position = max(lb, min(new_position, ub))  # Check boundaries

                wolves_positions[i][j] = new_position

    # Return the best solution found
    best_wolf_index = fitness_values.index(min(fitness_values))
    best_solution = wolves_positions[best_wolf_index]
    best_fitness = fitness_values[best_wolf_index]

    return best_solution, best_fitness

# Example usage
problem_size = 10
lb = -10  # Lower bound of the problem
ub = 10  # Upper bound of the problem
max_iterations = 100

best_solution, best_fitness = grey_wolf_optimizer(problem_size, objective_function, lb, ub, max_iterations)
print("Best solution:", best_solution)
print("Best fitness:", best_fitness)
2728 chars
66 lines