def sa_algorithm(nodes):
# Create an initial solution that we can improve upon.
number_of_nodes = len(nodes)
solution = [n for n in nodes]
# The temperature t. This is the most important parameter of the SA
# algorithm. It starts at a high temperature and is then slowly decreased.
# Both rate of decrease and initial values are parameters that need to be
# tuned to get a good solution.
# The initial temperature. This should be high enough to allow the
# algorithm to explore many sections of the search space. Set too high it
# will waste a lot of computation time randomly bouncing around the search
# space.
t = 100
# Length of the best solution so far.
l_min = total_length(nodes, solution)
best_solution = []
i = 0
while t > 0.1:
i = i + 1
# Given a solution we create a new solution
solution = sa_optimize_step(nodes, solution, number_of_nodes, t)
# every ~200 steps
if i >= 200:
i = 0
# Compute the length of the solution
l = total_length(nodes, solution)
#print " ", l, t, nn
# Lower the temperature.
# The slower we do this, the better then final solution
# but also the more times it takes.
t = t * 0.9995
# See if current solution is a better solution then the previous
# best one.
if l_min is None: # TODO: This can be removed, as l_min is set above.
l_min = l
elif l < l_min:
# Yup it is, remember it.
l_min = l
#print "++", l, t
best_solution = solution[:]
else:
pass
return best_solution
def read_problem(problem_file_name):
global xmax, ymax, xmin, ymin
nodes = []
with open(problem_file_name) as inpf:
first_line = inpf.readline()
node_count = int(first_line)
i = 0
for line in inpf:
parts = line.split()
x = float(parts[0])
y = float(parts[1])
if x > xmax: xmax = x
if y > ymax: ymax = y
if x < xmin: xmin = x
if y < ymin: ymin = y
nodes.append(Node(i, x, y))
i = i + 1
optfile = problem_file_name + ".opt"
if osp.exists(optfile):
with open(optfile) as f:
opttour = np.fromfile(f, dtype=int, sep='\n') - 1
print opttour, len(opttour)
s = np.array(nodes)[opttour]
if anim:
frame0(s, nodes, total_length(nodes, s), "opt")
return nodes
def sa_algorithm(nodes, number_of_nodes):
# Create an initial solution that we can improve upon.
solution = [n for n in nodes]
# The temperature t. This is the most important parameter of the SA
# algorithm. It starts at a high temperature and is then slowly decreased.
# Both rate of decrease and initial values are parameters that need to be
# tuned to get a good solution.
# The initial temperature. This should be high enough to allow the
# algorithm to explore many sections of the search space. Set too high it
# will waste a lot of computation time randomly bouncing around the search
# space.
t = 100
# Length of the best solution so far.
l_min = total_length( nodes, solution )
best_solution = []
i = 0
while t > 0.1:
i = i + 1
# Given a solution we create a new solution
solution = sa_optimize_step(nodes, solution, number_of_nodes, t)
# every ~200 steps
if i >= 200:
i = 0
# Compute the length of the solution
l = total_length( nodes, solution )
print " ", l, t, nn
# Lower the temperature.
# The slower we do this, the better then final solution
# but also the more times it takes.
t = t*0.9995
# See if current solution is a better solution then the previous
# best one.
if l_min is None: # TODO: This can be removed, as l_min is set above.
l_min = l
elif l < l_min:
# Yup it is, remember it.
l_min = l
print "++", l, t
best_solution = solution[:]
else:
pass
return best_solution
def solve(problem_file_name):
# This it to make sure we get the same answer each time.
random.seed(8111142)
solution_string = None
nodes = read_problem(problem_file_name)
solution = create_animation(nodes)
objective = total_length(nodes, solution)
solution_string = str(objective) + ' 0\n'
solution_string += ' '.join(map(lambda x: str(x.id), solution))
return solution_string
def solve(problem_file_name):
# This it to make sure we get the same answer each time.
random.seed(8111142)
solution_string = None
nodes = read_problem(problem_file_name)
solution = create_animation(nodes)
objective = total_length(nodes, solution)
solution_string = str(objective) + ' 0\n'
solution_string += ' '.join(map(lambda x: str(x.id), solution))
return solution_string
def solve( input_data ):
# This it to make sure we get the same anwser each time.
random.seed(8111142)
lines = input_data.split('\n')
node_count = int(lines[0])
nodes = []
for i in range(1, node_count+1):
line = lines[i]
parts = line.split()
nodes.append( Node( i-1, float(parts[0]), float(parts[1]) ) )
solution = create_animation( nodes )
objective = total_length( nodes, solution )
solution_string = str( objective ) + ' 0\n'
solution_string += ' '.join( map( lambda x: str(x.id), solution ) )
return solution_string
def frame(nodes, solution, sn, t, c, y, x, z, gain):
global pic
global nn
cities = [(n.x, n.y) for n in solution]
cities = np.array(cities)
cities2 = [(c.x, c.y), (y.x, y.y)]
cities3 = [(x.x, x.y), (z.x, z.y)]
cities2 = np.array(cities2)
cities3 = np.array(cities3)
plt.plot(cities[:, 0], cities[:, 1], 'bo-')
#plt.scatter(cities[:,0], cities[:,1],s=50,c='k')
if gain < 0:
plt.scatter(cities2[:, 0], cities2[:, 1], c='r', s=180)
plt.plot(cities2[:, 0], cities2[:, 1], c='r', linewidth=2)
plt.scatter(cities3[:, 0], cities3[:, 1], c='b', s=150)
plt.plot(cities3[:, 0], cities3[:, 1], c='r', linewidth=2)
else:
plt.scatter(cities2[:, 0], cities2[:, 1], c='g', s=180)
plt.plot(cities2[:, 0], cities2[:, 1], c='g', linewidth=2)
plt.scatter(cities3[:, 0], cities3[:, 1], c='b', s=150)
plt.plot(cities3[:, 0], cities3[:, 1], c='g', linewidth=2)
plt.axis([xmin - indent, xmax + indent, ymin - indent, ymax + indent])
plt.axis('off')
l_min = total_length(nodes, solution)
plt.title('(4) SA Temp {} Best Tour {}\nSwaps {} Gain {} '.format(
t, l_min, nn, gain))
plt.savefig(("%05d" % pic) + '.png')
plt.clf()
pic += 1
print pic
def frame4(nodes, solution, sn, c, y, x, z, gain):
global pic
global nn
l_min = total_length( nodes, solution )
cities = [ (n.x,n.y) for n in solution ]
cities = numpy.array( cities )
cities2 = [ (c.x,c.y), (y.x,y.y) ]
cities3 = [ (x.x,x.y), (z.x,z.y) ]
cities2 = numpy.array( cities2 )
cities3 = numpy.array( cities3 )
plt.plot(cities[:,0],cities[:,1],'bo-')
#plt.scatter(cities[:,0], cities[:,1],s=50,c='k')
if gain < 0:
plt.scatter(cities2[:,0], cities2[:,1],c='r',s=180)
plt.plot(cities2[:,0],cities2[:,1],c='r',linewidth=2)
plt.scatter(cities3[:,0], cities3[:,1],c='b',s=150)
plt.plot(cities3[:,0],cities3[:,1],c='r',linewidth=2)
else:
plt.scatter(cities2[:,0], cities2[:,1],c='g',s=180)
plt.plot(cities2[:,0],cities2[:,1],c='g',linewidth=2)
plt.scatter(cities3[:,0], cities3[:,1],c='b',s=150)
plt.plot(cities3[:,0],cities3[:,1],c='g',linewidth=2)
plt.axis( [-100,4100,-100,2100] )
plt.axis('off')
plt.title('(3) 2-Opt Tour {:6.1f}'.format(l_min))
plt.savefig( ("%05d" % pic)+'.png')
plt.clf()
pic += 1
print(pic)
def frame4( nodes, solution, sn, t, c,y,x,z, gain ):
global pic
global nn
l_min = total_length( nodes, solution )
cities = [ (n.x,n.y) for n in solution ]
cities = numpy.array( cities )
cities2 = [ (c.x,c.y), (y.x,y.y) ]
cities3 = [ (x.x,x.y), (z.x,z.y) ]
cities2 = numpy.array( cities2 )
cities3 = numpy.array( cities3 )
plt.plot(cities[:,0],cities[:,1],'bo-')
#plt.scatter(cities[:,0], cities[:,1],s=50,c='k')
if gain < 0:
plt.scatter(cities2[:,0], cities2[:,1],c='r',s=180)
plt.plot(cities2[:,0],cities2[:,1],c='r',linewidth=2)
plt.scatter(cities3[:,0], cities3[:,1],c='b',s=150)
plt.plot(cities3[:,0],cities3[:,1],c='r',linewidth=2)
else:
plt.scatter(cities2[:,0], cities2[:,1],c='g',s=180)
plt.plot(cities2[:,0],cities2[:,1],c='g',linewidth=2)
plt.scatter(cities3[:,0], cities3[:,1],c='b',s=150)
plt.plot(cities3[:,0],cities3[:,1],c='g',linewidth=2)
plt.axis( [-100,4100,-100,2100] )
plt.axis('off')
plt.title('(3) 2-Opt Tour {:6.1f}'.format(l_min))
plt.savefig( ("%05d" % pic)+'.png')
plt.clf()
pic += 1
print pic
def create_animation(nodes):
global nn
global l_min
number_of_nodes = len( nodes )
print('Size {}'.format( number_of_nodes ))
if do_greedy:
# Greedy Algorithm
print('Computing greedy path')
solution = greedy_algorithm(nodes)
else:
# For debugging
solution = [n for n in nodes]
if do_intro:
# Only cities
solution0 = [n for n in nodes]
for i in range(2, number_of_nodes):
s = solution0[0:i]
framec(s, number_of_nodes)
# Show all cities for an additional 20 frames.
for i in range(20):
framec(s, number_of_nodes)
# Animate the Random Search
for i in range(2, number_of_nodes):
s = solution0[0:i]
frame0(s, nodes, total_length(nodes, s), "(1) Random Path")
s = solution0
for i in range(60):
frame0(s, nodes, total_length(nodes, s), "(1) Random Path")
# Animate the Greedy Search
for i in range(2, number_of_nodes):
s = solution[0:i]
frame0(s, nodes, total_length(nodes, s), "(2) Greedy Search")
s = solution
for i in range(60):
frame0(s, nodes, total_length(nodes, s), "(2) Greedy Search")
# Under construction
if do_perry:
solution = miss_perry_s_compass(nodes, number_of_nodes)
for i in range(2, number_of_nodes):
s = solution[0:i]
frame0(s, nodes, total_length(nodes, s), "(1) Random Path")
for i in range(60):
frame0(solution, nodes, total_length(nodes, s), "(3) Miss Perry")
if do_2opt:
print("2-Opt")
# Run 2-Opt algorithm and create animation frames for each step
s = two_opt_algorithm(nodes, number_of_nodes)
# Show the best solution for an additional 60 frames.
for i in range(60):
frame0(s, nodes, total_length(nodes, s), "(4) 2-Opt")
if do_sa:
#=== Simulated Annealing
print("SA")
# Run SA algorithm and create animation frames for each step
s = sa_algorithm(nodes, number_of_nodes)
# Show the best solution for an additional 60 frames.
for i in range(60):
frame0(s, nodes, total_length(nodes, s), "(5) SA")
return s
def create_animation( nodes ):
global nn
global l_min
sn = len( nodes )
print 'Size {}'.format( sn )
if True:
# Greedy Algorithm
print 'Computing greedy path'
free_nodes = nodes[:]
solution = []
n = free_nodes[0]
free_nodes.remove(n)
solution.append( n )
while len( free_nodes ) > 0:
print len( free_nodes ),
min_l = None
min_n = None
for c in free_nodes:
l = length( c, n )
if min_l is None:
min_l = l
min_n = c
elif l < min_l:
min_l = l
min_n = c
solution.append(min_n)
free_nodes.remove(min_n)
n = min_n
else:
solution = [ n for n in nodes ]
if True:
# Only cities
solution0 = [ n for n in nodes ]
for i in range(2,sn):
s = solution0[0:i]
framec(s,sn)
for i in range(20):
framec(s,sn)
# Random Search
for i in range(2,sn):
s = solution0[0:i]
frame0(s,solution0, total_length(nodes,s), "(1) Random Path")
s = solution0
for i in range(60):
frame0(s,solution, total_length(nodes,s), "(1) Random Path")
# Greedy
for i in range(2,sn):
s = solution[0:i]
frame0(s,solution, total_length(nodes,s), "(2) Greedy Search")
s = solution
for i in range(60):
frame0(s,solution, total_length(nodes,s), "(2) Greedy Search")
print "2-Opt"
solution = [ n for n in nodes ]
t = 100
go = True
while go:
(go,solution) = optimize2opt( nodes, solution, sn, t )
s = solution
for i in range(60):
frame0(s,solution, total_length(nodes,s), "(3) 2-Opt")
print "SA"
solution = [ n for n in nodes ]
t = 100
l_min = total_length( nodes, solution )
best_solution = []
i = 0
while True:
i = i + 1
solution = optimize( nodes, solution, sn, t )
if i >= 200:
i = 0
l = total_length( nodes, solution )
print " ", l, t, nn
t = t*0.9995
if l_min is None:
l_min = l
elif l < l_min:
l_min = l
print "++", l, t
best_solution = solution[:]
else:
pass
if t < 0.1:
break
s = best_solution
for i in range(60):
frame0(s, solution, total_length(nodes,s), "(4) SA")
return best_solution
def create_animation(nodes):
global nn
global l_min
number_of_nodes = len( nodes )
print('Size {}'.format( number_of_nodes ))
if do_greedy:
# Greedy Algorithm
print 'Computing greedy path'
solution = greedy_algorithm(nodes)
else:
# For debugging
solution = [n for n in nodes]
if do_intro:
# Only cities
solution0 = [n for n in nodes]
for i in range(2, number_of_nodes):
s = solution0[0:i]
framec(s, number_of_nodes)
# Show all cities for an additional 20 frames.
for i in range(20):
framec(s, number_of_nodes)
# Animate the Random Search
for i in range(2, number_of_nodes):
s = solution0[0:i]
frame0(s, nodes, total_length(nodes, s), "(1) Random Path")
s = solution0
for i in range(60):
frame0(s, nodes, total_length(nodes, s), "(1) Random Path")
# Animate the Greedy Search
for i in range(2, number_of_nodes):
s = solution[0:i]
frame0(s, nodes, total_length(nodes, s), "(2) Greedy Search")
s = solution
for i in range(60):
frame0(s, nodes, total_length(nodes, s), "(2) Greedy Search")
# Under construction
if do_perry:
solution = miss_perry_s_compass(nodes, number_of_nodes)
for i in range(2, number_of_nodes):
s = solution[0:i]
frame0(s, nodes, total_length(nodes, s), "(1) Random Path")
for i in range(60):
frame0(solution, nodes, total_length(nodes, s), "(3) Miss Perry")
if do_2opt:
print("2-Opt")
# Run 2-Opt algorithm and create animation frames for each step
s = two_opt_algorithm(nodes, number_of_nodes)
# Show the best solution for an additional 60 frames.
for i in range(60):
frame0(s, nodes, total_length(nodes, s), "(4) 2-Opt")
if do_sa:
#=== Simulated Annealing
print("SA")
# Run SA algorithm and create animation frames for each step
s = sa_algorithm(nodes, number_of_nodes)
# Show the best solution for an additional 60 frames.
for i in range(60):
frame0(s, nodes, total_length(nodes, s), "(5) SA")
return s
def create_animation(nodes):
global nn
global l_min
sn = len(nodes)
print 'Size {}'.format(sn)
if True:
# Greedy Algorithm
print 'Computing greedy path'
free_nodes = nodes[:]
solution = []
n = free_nodes[0]
free_nodes.remove(n)
solution.append(n)
while len(free_nodes) > 0:
print len(free_nodes),
min_l = None
min_n = None
for c in free_nodes:
l = length(c, n)
if min_l is None:
min_l = l
min_n = c
elif l < min_l:
min_l = l
min_n = c
solution.append(min_n)
free_nodes.remove(min_n)
n = min_n
else:
solution = [n for n in nodes]
if True:
# Only cities
solution0 = [n for n in nodes]
for i in range(2, sn):
s = solution0[0:i]
framec(s, sn)
# Show all cities for an additional 20 frames.
for i in range(20):
framec(s, sn)
# Random Search
for i in range(2, sn):
s = solution0[0:i]
frame0(s, solution0, total_length(nodes, s), "(1) Random Path")
s = solution0
for i in range(60):
frame0(s, solution, total_length(nodes, s), "(1) Random Path")
# Greedy
for i in range(2, sn):
s = solution[0:i]
frame0(s, solution, total_length(nodes, s), "(2) Greedy Search")
s = solution
for i in range(60):
frame0(s, solution, total_length(nodes, s), "(2) Greedy Search")
print "2-Opt"
# Create an initial solution
solution = [n for n in nodes]
t = 100
go = True
# Try to optimize the solution with 2opt until
# no further optimization is possible.
while go:
(go, solution) = optimize2opt(nodes, solution, sn, t)
s = solution
for i in range(60):
frame0(s, solution, total_length(nodes, s), "(3) 2-Opt")
#=== Simulated Annealing
print "SA"
# Create an initial solution that we can improve upon.
solution = [n for n in nodes]
# The temperature t. This is the most important parameter
# of the SA algorithm. It starts at a high temperature and
# is then slowly decreased. Both rate of decrease and
# initial values are parameters that need to be tuned to
# get a good solution.
# The initial temperature.
# This should be high enough to allow the algorithm to
# explore many sections of the search space.
# Set too high it will waste a lot of computation time randomly
# bouncing around the search space.
t = 100
# Length of the best solution so far.
l_min = total_length(nodes, solution)
best_solution = []
i = 0
while True:
i = i + 1
solution = optimize(nodes, solution, sn, t)
# every ~200 steps
if i >= 200:
i = 0
# Compute the length of the solution
l = total_length(nodes, solution)
print " ", l, t, nn
# Lower the temperature.
# The slower we do this, the better then final solution
# but also the more times it takes.
t = t * 0.9995
# See if current solution is a better solution then the previous
# best one.
if l_min is None: # TODO: This can be removed, as l_min is set above.
l_min = l
elif l < l_min:
# Yup it is, remember it.
l_min = l
print "++", l, t
best_solution = solution[:]
else:
pass
if t < 0.1: # TODO: This should be part of the while condition.
break
# Show the best solution for an additional 60 frames.
s = best_solution
for i in range(60):
frame0(s, solution, total_length(nodes, s), "(4) SA")
return best_solution
