##################
# Have global path information
##################
# Scale individual tau's with inverse of ant's path length
# and add to delta tau
for ant in my_ants:
ant.scale_tau()
delta_tau = delta_tau + ant.dtau
L_list.append(ant.L)
if ant.L < L_min:
L_min = ant.L
path_min = ant.tab
L_list_a1.append(my_ants[0].L)
L_min_list.append(L_min)
# Update the global tau
#print(delta_tau)
tau = evap*tau+delta_tau
#print(tau)
# Prune to get rid of super small numbers
tau = tau_prune(tau,epp)
# Calculate branching
branch_list.append(np.average(tau_branch(tau,epp)))
#L_list = np.array(L_list)
print(L_min)
print(path_min)
# Plot output
plot_data(L_min_list, branch_list)
def tsp(colony, iters, n_mat):
'''
This function will cycle a colony through the cities
for a given number of iterations. It will return the updated
colony
Args: colony is a colony of ants; iters is number of iterations
to cycle through the TSP; n_mat is the visibility matrix of
the current city configuration
'''
# Initialization
N = len(n_mat[0,:]) # Number of cities
tau = np.ones((30,30)) # initial trail
epp = 0.0001 # Minimum branch level
evap = 0.5 # evaporation constant
my_ants = colony.ants
clock = 0
''' Run simulation '''
# Start the travels
for j in range(iters):
##################
# Re-initialize
##################
# clear global delta_tau
delta_tau = np.zeros((N,N))
# Reset ants for next tour
for ant in my_ants:
init_city = random.randint(0, N-1)
ant.reset(init_city)
##################
# Iterate over all cities
##################
for i in range (N):
# Move all the ants to a new city
for ant in my_ants:
ant.move_city(tau, n_mat)
# Increment the clock
clock = clock + 1
##################
# Have global path information
##################
# Scale individual tau's with inverse of ant's path length
# and add to delta tau
for ant in my_ants:
ant.scale_tau()
delta_tau = delta_tau + ant.dtau
# Update colony with new, travelled ants
colony.updateAnts(my_ants)
# Update the global tau
tau = evap*tau+delta_tau
# Prune to get rid of super small numbers
tau = tau_prune(tau,epp)
return colony
