材料力学优化算法：蚁群算法(ACO)：蚁群算法的参数调整与优化.Tex.header.docx

pheromone[best_path[i]][best_path[i + 1]] *= (1 - evaporation_rate)

pheromone[best_path[i]][best_path[i + 1]] += Q / total_distance(best_path)

return pheromone

def update_pheromone_local(pheromone, path, evaporation_rate, Q):

for i in range(len(path) - 1):

pheromone[path[i]][path[i + 1]] *= (1 - evaporation_rate)

pheromone[path[i]][path[i + 1]] += Q / distance(path[i], path[i + 1])

return pheromone

def parameter_tuning(experiments):

alpha = params['alpha']

beta = params['beta']

ant_count = params['ant_count']

#

for _ in range(iterations):

paths = generate_paths(pheromone, alpha, beta, ant_count)

pheromone = update_pheromone(pheromone, paths, evaporation_rate)

#

fitness = calculate_fitness(paths)

#

if fitness < best_fitness:

best_fitness = fitness

best_params = params

beta = 3.0 #

rho = 0.5 #

Q = 100 #

heuristic_info = np.array([[1.0, 2.0, 3.0],

[2.0, 3.0, 4.0],

[3.0, 4.0, 5.0]])

pheromone = np.ones(heuristic_info.shape)

for ant in range(num_ants):

solution = []

for i in range(heuristic_info.shape[0]):

#

probabilities = np.zeros(heuristic_info.shape[1])

for j in range(heuristic_info.shape[1]):

probabilities[j] = pheromone[i][j] ** alpha * heuristic_info[i][j] ** beta

probabilities /= np.sum(probabilities)

next_var = np.random.choice(range(heuristic_info.shape[1]), p=probabilities)

solution.append(next_var)

solutions.append(solution)

#

for solution in solutions:

cost = calculate_cost(solution)

best_cost = cost

best_solution = solution

for i in range(len(solution)):

pheromone[i][solution[i]] += Q / cost

#

pheromone *= (1 - rho)

best_solution, best_cost = ant_colony_optimization()