def ACO(number_loc, number_cars, D, max_it, start_nodes, resturants, customers):
# Parameters
number_of_orders = len(resturants)
n = number_loc
Q = 1
tau_0 = 10*Q/(n*np.mean(D)) # Initial Phromone
alpha = 1 # Phromone Exponential Weight
beta = 1 # Heuristic Exponential Weight
rho = 0.05 # Evaporation rate
# Multi cars parameteres
number_ants = number_cars
nodes_per_ant = multi_parameters(number_of_orders, number_ants)
print("Nodes per ant", nodes_per_ant)
# Initialization
tau = np.ones((n,n))*tau_0 # Phromone Matrix
eta = np.zeros((n,n)) # Heuristic Information Matrix
for i in range(n):
for j in range(n):
if i != j:
d_ij = D[i][j]
eta[i][j] = 1/d_ij
best_costs = np.zeros((number_ants, max_it)) # Array to hold best cost values
# Best ant
best_solutions = []
for d in range(number_ants):
a = Ant()
a.cost = np.inf
best_solutions.append(a)
# Iterations
for it in range(max_it):
# Create Ant colony
ant = []
for a in range(number_ants):
the_ant = Ant()
the_ant.add_to_tour(start_nodes[a])
ant.append(the_ant)
# Move ants
for k in range(number_ants):
other_ant_tours = find_other_tours(ant, number_ants, k)
while len(ant[k].tour) <= nodes_per_ant[k]*2:
i = ant[k].tour[-1]
P = (np.asarray(tau[i,:])**alpha)*(np.asarray(eta[i,:])**beta)
# Not allowed to choose a node that have been visited before
P[ant[k].tour] = 0
# Not allowed to choose a next node that is not a resturant
P[customers] = 0
# Not allowed to choose a next node where other ants has been at
P[other_ant_tours] = 0
# Only allowed to choose a next node that is a resutrant that has an order
for i in range(n):
if i not in resturants:
P[i] = 0
P = P/np.sum(P)
new_resturant = Roulette(P)
i=0
for resturant in resturants:
if resturant == new_resturant:
new_customer = customers[i]
i+=1
ant[k].add_to_tour(new_resturant)
ant[k].add_to_tour(new_customer)
ant[k].cost = tour_length(ant[k].tour, D)
cost_sum = 0
best_cost_sum = 0
for k in range(number_ants):
cost_sum += ant[k].cost
best_cost_sum += best_solutions[k].cost
if cost_sum < best_cost_sum:
for k in range(number_ants):
best_solutions[k] = ant[k]
# Update Phromones, this is where the cooperation is done
for k in range(number_ants):
tour = ant[k].tour.copy() # Erase copy() to add start node as end node
tour.append(tour[0])
number_of_orders_for_this_ant = nodes_per_ant[k]
for l in range(number_of_orders_for_this_ant):
i = tour[l]
j = tour[l+1]
tau[i][j] = tau[i][j]+Q/ant[k].cost
# Evaporation
tau = (1-rho)*tau
# Store Best Cost
for k in range(number_ants):
best_costs[k][it] = best_solutions[k].cost
# Show iteration information
total_best_cost = np.sum(best_costs, axis = 0)
print('Iteration', it, ': Best Cost = ', total_best_cost[it])
best_tours = info_and_tours(best_costs, start_nodes, number_ants, best_solutions)
return(best_tours)
def ACO(number_loc, number_cars, D, max_it, x, y):
# Parameters
n = number_loc
Q = 1
tau_0 = 10 * Q / (n * np.mean(D)) # Initial Phromone
alpha = 1 # Phromone Exponential Weight
beta = 1 # Heuristic Exponential Weight
rho = 0.05 # Evaporation rate
# Multi cars parameters
number_ants = number_cars
nodes_per_ant, start_nodes = multi_parameters(n, number_ants)
# Initialization
tau = np.ones((n, n)) * tau_0 # Phromone Matrix
eta = np.zeros((n, n)) # Heuristic Information Matrix
for i in range(n):
for j in range(n):
if i != j:
d_ij = D[i][j]
eta[i][j] = 1 / d_ij
best_costs = np.zeros(
(number_ants, max_it)) # Array to hold best cost values
# Best ant
best_solutions = []
for d in range(number_ants):
a = Ant()
a.cost = np.inf
best_solutions.append(a)
plt.ion()
for it in range(max_it):
# Create Ant colony
ant = []
for a in range(number_ants):
the_ant = Ant()
the_ant.add_to_tour(start_nodes[a])
ant.append(the_ant)
# Move ants
for k in range(number_ants):
other_ant_tours = find_other_tours(ant, number_ants, k)
while len(ant[k].tour) < nodes_per_ant[k]:
i = ant[k].tour[-1]
P = (np.asarray(tau[i, :])**alpha) * (np.asarray(eta[i, :])**
beta)
P[ant[k].
tour] = 0 # Cannot go back to its own already visited nodes
P[other_ant_tours] = 0 # Cannot go to nodes that other ants has been at
P = P / np.sum(P)
j = Roulette(P)
ant[k].add_to_tour(j)
ant[k].cost = tour_length(ant[k].tour, D)
cost_sum = 0
best_cost_sum = 0
for k in range(number_ants):
cost_sum += ant[k].cost
best_cost_sum += best_solutions[k].cost
if cost_sum < best_cost_sum:
for k in range(number_ants):
best_solutions[k] = ant[k]
# Update Phromones
for k in range(number_ants):
tour = ant[k].tour.copy(
) # Erase copy() to add start node as end node
tour.append(tour[0])
for l in range(nodes_per_ant[k]):
i = tour[l]
j = tour[l + 1]
tau[i][j] = tau[i][j] + Q / ant[k].cost
# Evaporation
tau = (1 - rho) * tau
# Store Best Cost
for k in range(number_ants):
best_costs[k][it] = best_solutions[k].cost
# Show iteration information
total_best_cost = np.sum(best_costs, axis=0)
print('Iteration', it, ': Best Cost = ', total_best_cost[it])
# Plot best tours for each iteration, showing the progress
best_it_tours = []
for i in range(number_ants):
best_it_tours.append(best_solutions[i].tour)
fig = plt.figure(1, figsize=(14, 7.5))
plot_solution(x, y, best_it_tours, start_nodes)
plt.pause(10**(-16))
plt.clf()
best_tours = info_and_tours(best_costs, start_nodes, number_ants,
best_solutions)
#plot_solution(x,y, best_tours)
#plt.show()
return (best_tours)
