def greedy_Random_Construct():
sn = [0]*len(bp.OBJs) # initial state
c = True # continue flag
while c:
cs = sn # storing current state
c, sn = select_Random(bp.state_Expansion(cs))
return cs
def hill_Climbing():
sn = [0] * len(bp.OBJs) # initial state
c = True # continue flag
while c:
cs = sn # storing current state
c, sn = select_Best(bp.state_Expansion(cs))
return cs
def neightborhood(s):
neig = []
for i in bp.state_Expansion(s): # add all valid states from the expansion of the given state
if bp.state_Verify(i):
neig.append(i)
for i in bp.state_Retract(s): # add all valid states from the retraction of the given state
if bp.state_Verify(i):
neig.append(i)
return neig
def select_Best_States(n, st):
si = bp.state_Expansion(st)
si = [[bp.state_Value(s),s] for s in si]
si.sort(reverse=True)
si = [i[1] for i in si]
bss = []
for i in filter(bp.state_Verify, si):
bss.append(i)
return bss[:n]
def select_Best_States(n, st, T, OBJs):
si = bp.state_Expansion(st)
si = [[bp.state_Value(s, OBJs), s] for s in si]
si.sort(reverse=True)
si = [i[1] for i in si]
bss = []
for i in si:
if bp.state_Verify(i, T, OBJs):
bss.append(i)
return bss[:n]
def hill_Climbing(T, OBJs, execTime, *args):
sn = [0] * len(OBJs) # initial state
c = True # continue flag
start = time()
while c:
if time() - start > execTime:
break
cs = sn # storing current state
c, sn = select_Best(bp.state_Expansion(cs), T, OBJs)
return cs
def neightborhood(s):
neig = []
for i in bp.state_Expansion(s): # add all valid states from the expansion of the given state
if bp.state_Verify(i):
neig.append(i)
for i in neig: # adding all valid retractions of each state currently in the neightborhood
for j in bp.state_Retract(i):
if bp.state_Verify(j):
neig.append(j)
for i in bp.state_Retract(s): # add all valid states from the retraction of the given state
if bp.state_Verify(i):
neig.append(i)
return neig
def neightborhood(s, T, OBJs):
neig = []
for i in bp.state_Expansion(
s): # adding all valid expansions of the given state
if bp.state_Verify(i, T, OBJs):
neig.append(i)
# for i in neig: # adding all valid retractions of each state currently in the neightborhood
# for j in bp.state_Retract(i):
# if bp.state_Verify(j, T, OBJs):
# neig.append(j)
for i in bp.state_Retract(
s): # adding all valid retractions of the given state
if bp.state_Verify(i, T, OBJs):
neig.append(i)
return neig
def branch_n_bound(use_opt_estimate=True):
bs = triv_solution() # best state found
sv = bp.state_Value(bs) # value of best bs
pq.insert(0, [0] * len(bp.OBJs)) # pushing initial state in queue
while not pq.isEmpty():
cs = pq.remove() # current state
si = bp.state_Expansion(cs) # expansion of current state
for s in si:
if bp.state_Verify(s):
if use_opt_estimate: # selecting estimate method
est = opt_Estimate(s)
else:
est = n_Opt_Estimate(s)
if bp.state_Value(est) > sv:
if bp.state_Value(s) > sv:
bs = s # updating best bs
sv = bp.state_Value(s) # updating best value
pq.insert(bp.state_Value(s), s)
return bs
def greedy_Random_Construct(s, numBest, T, OBJs, start, execTime):
sn = [0] * len(OBJs) # initial state
while True:
if time() - start > execTime:
break
additions = bp.state_Expansion(
sn) # list of possible additions to state sn
best = [] # list of best additions
# Selecting the best additions:
i = 0
# add states to best until best is full or there are no more states:
while len(best) < numBest and i < len(additions):
if bp.state_Verify(additions[i], T, OBJs):
best.append(additions[i])
if len(best) >= len(additions):
break
i += 1
# if best is not empty chose a state by the roulette method:
if len(best) > 0:
c = roulette(best, OBJs)
sn = c
continue
break
return sn
