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local_search.py
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local_search.py
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import math
import random
from helper_functions import randomOptions
LOCAL_SEARCH_COEFFICIENT = 18.76
ANNEALING_TEMPERATURE_FACTOR = 1000000
def eager_search(sln, iterations_coeff=LOCAL_SEARCH_COEFFICIENT):
crnt_it = 0.0
max_it_f = iterations_coeff * sln.n
while crnt_it < max_it_f:
crnt_it += 1.0
mutation_factor = crnt_it / max_it_f
aux_sln = sln.copy()
for i in xrange(0, int(math.ceil(mutation_factor * sln.n))):
aux_sln.randomize()
if aux_sln.cost < sln.cost:
sln.permutation = aux_sln.permutation
sln.flows = aux_sln.flows
sln.cost = aux_sln.cost
crnt_it = 0
break
del aux_sln
return sln
def annealing(sln, t_max=ANNEALING_TEMPERATURE_FACTOR):
t = t_max
k = 0.0
while t > 0.0:
aux_sln = sln.copy()
aux_sln.randomize(sln.n if t > sln.n else int(t))
diff_cost = sln.cost - aux_sln.cost
if diff_cost > 0 or \
math.exp(float(diff_cost) / t) > random.uniform(0,1):
sln.permutation = aux_sln.permutation
sln.flows = aux_sln.flows
sln.cost = aux_sln.cost
del aux_sln
k += 1.0
t = math.floor(t_max / (1.0 + 300000.0 * math.log10(1 + k)))
return sln
def search(sln, iterations_coeff=LOCAL_SEARCH_COEFFICIENT):
for _ in xrange(0, int(iterations_coeff * sln.n)):
cities = randomOptions(sln.n, k=2)
aux_cost = sln.cost
sln.exchangeFacilities(cities[0], cities[1])
if aux_cost < sln.cost:
sln.exchangeFacilities(cities[0], cities[1])
return sln