def test_tabu_search(self):
n = 50
m = 100
loops = 30
gen = Generator(n, m)
status = Status(gen.p, gen.c, gen.d)
status.set_cf(gen.cf)
status.set_emin(gen.emin)
status.set_emax(gen.emax)
status.set_age(gen.age)
status.set_mage(gen.mage)
status.set_male(gen.male)
status.set_sratio(gen.sratio)
status.set_cmin(gen.cmin)
status.allowed_time = 300
status.improving = 1
status.delta = 1
status.restarts = 20
status.attempts = 100000
status.set_status()
init = initial_solution_bottom_up(gen.p, gen.c, gen.d)
results = {}
tenures = [0, 2, 5, 10]
legal = [is_legal_not_tabu, is_legal_not_tabu_aspiration]
for i in range(loops):
for tenure in tenures:
status.tenure = tenure
print("Tenure value: {}".format(tenure))
for aspiration in legal:
print("Legal function: {}".format(aspiration))
start = time.time()
a, s, score = tabu_search(init, objective_compound_incr, neighborhood_all, aspiration, selection_first_improvement)
elapsed = time.time() - start
print("Solution (first improvement): {} in {} sec ({} attempts)".format(score, elapsed, a))
status.k = 3
start = time.time()
a_, s_, score_ = tabu_search(init, objective_compound_incr, neighborhood_all, aspiration, selection_best_k)
elapsed_ = time.time() - start
print("Solution (best 3): {} in {} sec ({} attempts)".format(score_, elapsed_, a_))
status.k = 5
start = time.time()
a__, s__, score__ = tabu_search(init, objective_compound_incr, neighborhood_all, aspiration, selection_best_k)
elapsed__ = time.time() - start
print("Solution (best 5): {} in {} sec ({} attempts)".format(score__, elapsed__, a__))
if i == 0:
results[(tenure, aspiration.__name__, "first_improvement")] = [(score, elapsed, a)]
results[(tenure, aspiration.__name__, "best_3")] = [(score_, elapsed_, a_)]
results[(tenure, aspiration.__name__, "best_5")] = [(score__, elapsed__, a__)]
else:
results[(tenure, aspiration.__name__, "first_improvement")].append((score, elapsed, a))
results[(tenure, aspiration.__name__, "best_3")].append((score_, elapsed_, a_))
results[(tenure, aspiration.__name__, "best_5")].append((score__, elapsed__, a__))
for k, v in sorted(results.items()):
score_all, elapsed_all, a_all = 0, 0, 0
for score, elapsed, a in v:
score_all += score
elapsed_all += elapsed
a_all += a
print("{} -> {:.4f} in {:.2f} sec, {} attempts (average on {} iterations)".format(k, score_all / loops, elapsed_all / loops, a_all / loops, loops))
status.k = 3
status.tenure = 2
status.improving = 5
status.delta = 1
status.allowed_time = 3000
status.set_status()
'''launching search'''
if verbose:
print("Launching {} search... ({} seconds)".format(("light" if len(declines) > 0 else "heavy"), status.allowed_time))
s, score = None, 0
if len(declines) > 0:
status.attempts = len(declines) * len(events)
status.tenure = 0
init = initial_solution_confirmed_only()
s, score = tabu_search(initial_solution_bottom_up(status.p, status.c, status.d), objective_compound_incr, neighborhood_add, is_legal_not_tabu, selection_best_k)
else:
s, score = tabu_search_restarts(initial_solution_bottom_up, objective_compound_incr, neighborhood_all, is_legal_not_tabu, selection_best_k)
print("Solution score: {}".format(score))
print("Solution: {}".format(s))
'''connecting'''
con = connect(params)
if not con:
sys.exit(1)
cur = con.cursor()
'''updating database'''
if verbose:
print("--- Updating database...")
update_data(cur, uids, eids, declines, s)
def test_tabu_search(self):
n = 50
m = 100
loops = 30
gen = Generator(n, m)
status = Status(gen.p, gen.c, gen.d)
status.set_cf(gen.cf)
status.set_emin(gen.emin)
status.set_emax(gen.emax)
status.set_age(gen.age)
status.set_mage(gen.mage)
status.set_male(gen.male)
status.set_sratio(gen.sratio)
status.set_cmin(gen.cmin)
status.allowed_time = 300
status.improving = 1
status.delta = 1
status.restarts = 20
status.attempts = 100000
status.set_status()
init = initial_solution_bottom_up(gen.p, gen.c, gen.d)
results = {}
tenures = [0, 2, 5, 10]
legal = [is_legal_not_tabu, is_legal_not_tabu_aspiration]
for i in range(loops):
for tenure in tenures:
status.tenure = tenure
print("Tenure value: {}".format(tenure))
for aspiration in legal:
print("Legal function: {}".format(aspiration))
start = time.time()
a, s, score = tabu_search(init, objective_compound_incr,
neighborhood_all, aspiration,
selection_first_improvement)
elapsed = time.time() - start
print(
"Solution (first improvement): {} in {} sec ({} attempts)"
.format(score, elapsed, a))
status.k = 3
start = time.time()
a_, s_, score_ = tabu_search(init, objective_compound_incr,
neighborhood_all, aspiration,
selection_best_k)
elapsed_ = time.time() - start
print(
"Solution (best 3): {} in {} sec ({} attempts)".format(
score_, elapsed_, a_))
status.k = 5
start = time.time()
a__, s__, score__ = tabu_search(init,
objective_compound_incr,
neighborhood_all,
aspiration,
selection_best_k)
elapsed__ = time.time() - start
print(
"Solution (best 5): {} in {} sec ({} attempts)".format(
score__, elapsed__, a__))
if i == 0:
results[(tenure, aspiration.__name__,
"first_improvement")] = [(score, elapsed, a)]
results[(tenure, aspiration.__name__,
"best_3")] = [(score_, elapsed_, a_)]
results[(tenure, aspiration.__name__,
"best_5")] = [(score__, elapsed__, a__)]
else:
results[(tenure, aspiration.__name__,
"first_improvement")].append(
(score, elapsed, a))
results[(tenure, aspiration.__name__,
"best_3")].append((score_, elapsed_, a_))
results[(tenure, aspiration.__name__,
"best_5")].append((score__, elapsed__, a__))
for k, v in sorted(results.items()):
score_all, elapsed_all, a_all = 0, 0, 0
for score, elapsed, a in v:
score_all += score
elapsed_all += elapsed
a_all += a
print(
"{} -> {:.4f} in {:.2f} sec, {} attempts (average on {} iterations)"
.format(k, score_all / loops, elapsed_all / loops,
a_all / loops, loops))
def test_tabu_vs_restarts(self):
n = 50
m = 100
loops = 1
gen = Generator(n, m)
status = Status(gen.p, gen.c, gen.d)
status.set_cf(gen.cf)
status.set_emin(gen.emin)
status.set_emax(gen.emax)
status.set_age(gen.age)
status.set_mage(gen.mage)
status.set_male(gen.male)
status.set_sratio(gen.sratio)
status.set_cmin(gen.cmin)
#status.chosen_ones = gen.chosen_ones
status.allowed_time = 3600
status.restarts = 20
status.k = 3
status.attempts = 10000
status.set_status()
init = initial_solution_bottom_up(gen.p, gen.c, gen.d)
results = {}
for i in range(loops):
status.improving = 5
status.delta = 1
status.tenure = 2
start = time.time()
a, s, score = tabu_search_restarts(initial_solution_bottom_up,
objective_compound_incr,
neighborhood_all,
is_legal_not_tabu,
selection_best_k)
elapsed = time.time() - start
if i == 0:
results[("restarts", "stagn=5", "no aspiration")] = [(a, score,
elapsed)]
else:
results[("restarts", "stagn=5", "no aspiration")].append(
(a, score, elapsed))
print(
"Score restarts (tenure=5): {:.4f} in {:.2f} sec ({:.2f} iterations)"
.format(score, elapsed, a))
status.tenure = 5
start = time.time()
a, s, score = tabu_search(init, objective_compound_incr,
neighborhood_all,
is_legal_not_tabu_aspiration,
selection_best_k, 0)
elapsed = time.time() - start
if i == 0:
results[("tabu", 5, "aspiration")] = [(a, score, elapsed)]
else:
results[("tabu", 5, "aspiration")].append((a, score, elapsed))
print(
"Score tabu (tenure=5, aspiration): {:.4f} in {:.2f} sec ({:.2f} iterations)"
.format(score, elapsed, a))
status.tenure = 0
start = time.time()
a, s, score = tabu_search(init, objective_compound_incr,
neighborhood_all, is_legal_not_tabu,
selection_best_k, 0)
elapsed = time.time() - start
if i == 0:
results[("tabu", 0, "no aspiration")] = [(a, score, elapsed)]
else:
results[("tabu", 0, "no aspiration")].append(
(a, score, elapsed))
print(
"Score tabu (tenure=0, no aspiration): {:.4f} in {:.2f} sec ({:.2f} iterations)"
.format(score, elapsed, a))
status.tenure = 2
status.improving = 1
start = time.time()
a, s, score = tabu_search_restarts(initial_solution_bottom_up,
objective_compound_incr,
neighborhood_all,
is_legal_not_tabu_aspiration,
selection_best_k)
elapsed = time.time() - start
if i == 0:
results[("restarts", "stagn=1", "aspiration")] = [(a, score,
elapsed)]
else:
results[("restarts", "stagn=1", "aspiration")].append(
(a, score, elapsed))
print(
"Score restarts (tenure=10): {:.4f} in {:.2f} sec ({:.2f} iterations)"
.format(score, elapsed, a))
for k, v in sorted(results.items()):
score_all, elapsed_all, attempts_all = 0, 0, 0
for a, score, elapsed in v:
score_all += score
elapsed_all += elapsed
attempts_all += a
print(
"{} -> {:.4f} in {:.2f} sec, {:.2f} attempts (average on {} iterations)"
.format(k, score_all / loops, elapsed_all / loops,
attempts_all / loops, loops))
if __name__ == '__main__':
for tabu_list_size in [5]:
# initial_state = np.arange(20)
# BEST STATE: 17, 5, 7, 1, 6, 19, 15, 20, 8, 13, 4, 2, 12, 11, 16, 18, 14, 10, 3, 9
# TRIAL 1: INPUT: 6 2 3 19 10 11 15 1 12 18 16 17 5 8 7 13 14 4 0 9 => OUTPUT: 1287.0
initial_state = deserialize(
'15 6 11 7 10 5 0 13 16 18 9 14 17 2 12 19 3 8 4 1')
# initial_state = np.array(list(map(
# lambda x: x-1, [17, 7, 5, 1, 6, 4, 20, 8, 11, 13, 19, 15, 12, 10, 16, 18, 2, 14, 3, 9])))
# initial_state = np.array(
# list(map(lambda x: x-1, [2, 9, 8, 12, 1, 7, 11, 19, 13, 10, 5, 18, 3, 17, 14, 15, 6, 16, 20, 4])))
# np.random.shuffle(initial_state)
initial_state = serialize(initial_state)
stopping_condition = gen_stopping_condition(500)
tabu_search = configure_tabu_search(stopping_condition, get_neighbors,
evaluate, tabu_list_size)
print(f'Tabu List Size: {tabu_list_size}')
print(f'Initial State: {initial_state}')
print(f'Initial Cost: {-evaluate(initial_state)}')
final_state = tabu_search(initial_state)
print(f'Final State: {final_state}')
print(f'Final Cost: {-evaluate(final_state)}')
print()
def test_tabu_vs_restarts(self):
n = 50
m = 100
loops = 1
gen = Generator(n, m)
status = Status(gen.p, gen.c, gen.d)
status.set_cf(gen.cf)
status.set_emin(gen.emin)
status.set_emax(gen.emax)
status.set_age(gen.age)
status.set_mage(gen.mage)
status.set_male(gen.male)
status.set_sratio(gen.sratio)
status.set_cmin(gen.cmin)
#status.chosen_ones = gen.chosen_ones
status.allowed_time = 3600
status.restarts = 20
status.k = 3
status.attempts = 10000
status.set_status()
init = initial_solution_bottom_up(gen.p, gen.c, gen.d)
results = {}
for i in range(loops):
status.improving = 5
status.delta = 1
status.tenure = 2
start = time.time()
a, s, score = tabu_search_restarts(initial_solution_bottom_up, objective_compound_incr, neighborhood_all, is_legal_not_tabu, selection_best_k)
elapsed = time.time() - start
if i == 0:
results[("restarts", "stagn=5", "no aspiration")] = [(a, score, elapsed)]
else:
results[("restarts", "stagn=5", "no aspiration")].append((a, score, elapsed))
print("Score restarts (tenure=5): {:.4f} in {:.2f} sec ({:.2f} iterations)".format(score, elapsed, a))
status.tenure = 5
start = time.time()
a, s, score = tabu_search(init, objective_compound_incr, neighborhood_all, is_legal_not_tabu_aspiration, selection_best_k, 0)
elapsed = time.time() - start
if i == 0:
results[("tabu", 5, "aspiration")] = [(a, score, elapsed)]
else:
results[("tabu", 5, "aspiration")].append((a, score, elapsed))
print("Score tabu (tenure=5, aspiration): {:.4f} in {:.2f} sec ({:.2f} iterations)".format(score, elapsed, a))
status.tenure = 0
start = time.time()
a, s, score = tabu_search(init, objective_compound_incr, neighborhood_all, is_legal_not_tabu, selection_best_k, 0)
elapsed = time.time() - start
if i == 0:
results[("tabu", 0, "no aspiration")] = [(a, score, elapsed)]
else:
results[("tabu", 0, "no aspiration")].append((a, score, elapsed))
print("Score tabu (tenure=0, no aspiration): {:.4f} in {:.2f} sec ({:.2f} iterations)".format(score, elapsed, a))
status.tenure = 2
status.improving = 1
start = time.time()
a, s, score = tabu_search_restarts(initial_solution_bottom_up, objective_compound_incr, neighborhood_all, is_legal_not_tabu_aspiration, selection_best_k)
elapsed = time.time() - start
if i == 0:
results[("restarts", "stagn=1", "aspiration")] = [(a, score, elapsed)]
else:
results[("restarts", "stagn=1", "aspiration")].append((a, score, elapsed))
print("Score restarts (tenure=10): {:.4f} in {:.2f} sec ({:.2f} iterations)".format(score, elapsed, a))
for k, v in sorted(results.items()):
score_all, elapsed_all, attempts_all = 0, 0, 0
for a, score, elapsed in v:
score_all += score
elapsed_all += elapsed
attempts_all += a
print("{} -> {:.4f} in {:.2f} sec, {:.2f} attempts (average on {} iterations)".format(k, score_all / loops, elapsed_all / loops, attempts_all / loops, loops))
