sol,rmn,card = iterated_expansion(nodes, adj, expand_dyn_deg, max_iterations, report_sol)
elif method == "plateau_rand":
sol,rmn,card = multistart_local_search(nodes, adj, expand_rand, max_iterations, length, report_sol)
elif method == "plateau_stat_deg":
sol,rmn,card = multistart_local_search(nodes, adj, expand_stat_deg, max_iterations, length, report_sol)
elif method == "plateau_dyn_deg":
sol,rmn,card = multistart_local_search(nodes, adj, expand_dyn_deg, max_iterations, length, report_sol)
elif method == "ltm_rand":
sol,rmn,card = ltm_search(nodes, adj, expand_rand, max_iterations, length, report_sol)
elif method == "ltm_stat_deg":
sol,rmn,card = ltm_search(nodes, adj, expand_stat_deg, max_iterations, length, report_sol)
elif method == "ltm_dyn_deg":
sol,rmn,card = ltm_search(nodes, adj, expand_dyn_deg, max_iterations, length, report_sol)
else:
if method != "hybrid":
print("unknown method; using hybrid version")
degree = [len(adj[i]) for i in nodes] # used on 'expand_sta_deg'
sol,rmn,card = hybrid(nodes, adj, max_iterations, length, report_sol)
# import profile
# profile.run('sol,rmn,card = exp_rand(nodes, adj, max_iterations)')
# profile.run('sol,rmn,card = exp_plateau(nodes, adj, max_iterations)')
xcard, xinfeas, xb = evaluate(nodes, adj, sol)
assert card == xcard and xinfeas == 0
print()
print("final solution: z =", len(sol))
print(sorted(sol))
print()
def ts_intens_divers(nodes, adj, sol, max_iter, tabulen, report):
"""Execute a tabu search run using intensification/diversification."""
n = len(nodes)
tabu = [0 for i in nodes]
card, infeas, b = evaluate(nodes, adj, sol)
assert infeas == 0
bestsol, bestcard, bestb = set(sol), card, list(b)
D = 1 # self-tuning diversification parameter
count = 0 # counter for consecutive non-improving iterations
lastcard = card
if LOG:
print("iter:", 0, "non-impr: %d/%d" % (count,D), \
"\tcard: %d (%d conflicts)" % (card,infeas), "/ best:", bestcard) # , "\t", sol
for it in range(max_iter):
tabuIN = 1 + int(tabulen / 100. *
card) # update tabu parameter for inserting vertices
tabuOUT = 1 + int(
tabulen / 100. *
(n - card)) # update tabu parameter for removing vertices
if infeas == 0: # solution is feasible, add a new vertex
infeas += move_in(nodes, adj, sol, b, tabu, tabuIN, tabuOUT, it)
card += 1
else: # solution is infeasible, remove a vertex
infeas += move_out(nodes, adj, sol, b, tabu, tabuIN, tabuOUT, it)
card -= 1
if LOG:
print("iter:", it+1, "non-impr: %d/%d" % (count,D), \
"\tcard: %d (%d conflicts)" % (card,infeas), "/ best:", bestcard) # , "\t", sol
if infeas == 0 and card > bestcard:
# improved best found solution, intensify search
bestsol, bestcard, bestb = set(sol), card, list(b)
if report:
report(card, "iter:%d" % it)
if LOG:
print("*** intensifying: clearing tabu list***")
tabu = [min(tabu[i], it) for i in nodes] # clear tabu list
count = 0
elif infeas == 0 and card > lastcard:
count = 0 # reset non-improving iterations counter
else:
count += 1
if count > D: # exceeded allowed non-improving iterations, restart int/div cycle
if D % 2 == 0: # intensification: switch to best found solution
if LOG:
print(
"*** intensifying: switching to best found solution ***"
)
sol, card, b = set(bestsol), bestcard, list(bestb)
infeas = 0
# keep tabu list unchanged, for ensuring a different path
else: # diversification: construct maximal stable set from less used vertex
if LOG:
print(
"*** diversifying: constructing maximal set from less used vertex ***"
)
# use the tabu history as long-term memory:
cand = []
mintabu = Infinity
for j in set(
nodes) - sol: # find less used vertex (smallest tabu)
if tabu[i] < mintabu:
cand = [i]
if tabu[i] == mintabu:
cand.append(i)
v = random.choice(cand)
sol = diversify(nodes, adj, v)
card, infeas, b = evaluate(nodes, adj, sol)
if infeas == 0 and card > bestcard:
bestsol, bestcard, bestb = set(sol), card, list(b)
if report:
report(card, "iter:%d" % it)
tabu = [min(tabu[i], it) for i in nodes] # clear tabu list
count = 0 # reset counter
D += 1 # increase self-tuning parameter
if infeas == 0:
lastcard = card
# check solution correctness:
# xcard, xinfeas, xb = evaluate(nodes, adj, bestsol)
# assert bestcard == xcard and xinfeas == 0
return bestsol, bestcard