def run_local_search(seconds):
print('Running Local Search algoritm for ' + str(seconds) + ' seconds...')
print()
ls = LocalSearch(states, seconds, inc_support, dec_support)
ls.run()
print('Found optimal route with value of ' + str(ls.best_solution.value) +
'.')
print(
str(ls.best_solution.calculate_real_value()) +
' electoral votes were collected.')
ls.best_solution.print()
print()
def benchmark():
REPEATS = 10
SECONDS = [5, 10, 30, 60, 300, 1200]
for seconds in SECONDS:
v = 0
time_s = datetime.now()
for k in range(REPEATS):
rs = RandomSearch(states, seconds, inc_support, dec_support)
rs.run()
v += rs.best_solution.value
time_e = datetime.now()
tt = (time_e - time_s).total_seconds()
print_csv('Random Search', str(seconds), str(v / REPEATS),
str(tt / REPEATS))
for seconds in SECONDS:
v = 0
time_s = datetime.now()
for k in range(REPEATS):
ls = LocalSearch(states, seconds, inc_support, dec_support)
ls.run()
v += ls.best_solution.value
time_e = datetime.now()
tt = (time_e - time_s).total_seconds()
print_csv('Local Search', str(seconds), str(v / REPEATS),
str(tt / REPEATS))
for seconds in SECONDS:
for initial_cadence in [10, 25, 50]:
for critical_event in [10, 25, 50]:
v = 0
time_s = datetime.now()
for k in range(REPEATS):
ts = TabuSearch(states, seconds, initial_cadence,
critical_event, inc_support, dec_support)
ts.run()
v += ts.best_solution.value
time_e = datetime.now()
tt = (time_e - time_s).total_seconds()
print_csv('Tabu Search', str(seconds), str(initial_cadence),
str(critical_event), str(v / REPEATS),
str(tt / REPEATS))
for crossover in ['pmx', 'ox']:
for mutate in ['transposition', 'insertion', 'inversion']:
for seconds in SECONDS:
for population_size in [10, 25, 50]:
v = 0
time_s = datetime.now()
for k in range(REPEATS):
ga = GeneticAlgorithm(states, seconds, population_size,
crossover, mutate, inc_support,
dec_support)
ga.run()
v += ga.best_solution.value
time_e = datetime.now()
tt = (time_e - time_s).total_seconds()
print_csv('Genetic Algorithm ' + crossover + ' ' + mutate,
str(seconds), str(population_size),
str(v / REPEATS), str(tt / REPEATS))
for initial_temperature in [100, 500, 1000]:
for cooling_coefficient in [0.9, 0.99, 0.999, 0.9999]:
for minimal_temperature in [
initial_temperature * 0.25, initial_temperature * 0.5,
initial_temperature * 0.75
]:
v = 0
time_s = datetime.now()
for k in range(REPEATS):
sa = SimulatedAnnealing(states, initial_temperature,
cooling_coefficient,
minimal_temperature, inc_support,
dec_support)
sa.run()
v += sa.best_solution.value
time_e = datetime.now()
tt = (time_e - time_s).total_seconds()
print_csv('Simulated Annealing', str(initial_temperature),
str(cooling_coefficient), str(minimal_temperature),
str(v / REPEATS), str(tt / REPEATS))