材料力学优化算法：蚁群算法(ACO)：ACO算法的数学模型建立.Tex.header.docx

import numpy as np

import random

materials = {

1: {'thickness': 0.5, 'modulus': 100},

2: {'thickness': 0.6, 'modulus': 120},

3: {'thickness': 0.7, 'modulus': 140},

return 0 #

total_modulus = sum([materials[l]['modulus'] * materials[l]['thickness'] for l in layers])

return total_modulus

num_ants = 10

num_iterations = 100

alpha = 1.0 #

beta = 5.0 #

rho = 0.5 #

Q = 100 #

for ant in range(num_ants):

path = []

remaining_materials = list(materials.keys())

while len(path) < 6:

#

probabilities = []

for material in remaining_materials:

prob = (pheromone[len(path), material] ** alpha) * ((1 / materials[material]['thickness']

) ** beta)

probabilities.append(prob)

remaining_materials.remove(chosen_material)

ant_paths.append(path)

path_stiffness = stiffness(path)

for i in range(6):

best_path = max(ant_paths, key=stiffness)

[15, 22, 25, 0, 12],

[25, 20, 16, 12, 0]])

pheromone_matrix = np.ones(distance_matrix.shape)

alpha = 1 #

beta = 5 #

rho = 0.5 #

Q = 100 #

ant_count = 50

paths = []

for _ in range(ant_count):

path = [random.randint(0, len(distance_matrix)-1)]

path.append(next_city)

paths.append(path)

best_path = path

update_pheromones(path, path_distance)

return best_path, best_distance

def select_next_city(current_city):

probabilities = []

for city in range(len(distance_matrix)):

if city not in path:

probability = (pheromone_matrix[current_city][city]**alpha) * ((1/distance_matrix[curren

t_city][city])**beta)

probabilities.append(probability)

probabilities = np.array(probabilities)

probabilities /= probabilities.sum()

return next_city

distance = 0

distance += distance_matrix[path[i]][path[i+1]]

distance += distance_matrix[path[-1]][path[0]]

return distance

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

pheromone_matrix[path[i]][path[i+1]] += Q/path_distance

pheromone_matrix *= rho