def MainProcess(elapsed_time, simulation_table):
start_time = time.time()
IND_SIZE = (EXHIBITION_HALL['number_of_zones']) * \
(int(SIMULATION_TIME_VARIABLE['schedule_horizon_width_in_setpoint_first_minutes']/SIMULATION_TIME_VARIABLE['first_setpoint_control_interval_in_minutes']) + \
int(SIMULATION_TIME_VARIABLE['schedule_horizon_width_in_setpoint_second_minutes']/SIMULATION_TIME_VARIABLE['second_setpoint_control_interval_in_minutes']) + \
int(SIMULATION_TIME_VARIABLE['schedule_horizon_width_in_ahu_on_off_minutes'] / SIMULATION_TIME_VARIABLE['on_off_control_interval_in_minutes']))
POP_SIZE = 50
NEVAL = 5000
# crossover parameter
CXPB = 1
CXETA = 1
CXLOW = 0
CXUP = 1
# mutation parameter
MUTPB = 1
MUTETA = 1
MUTLOW = 0
MUTUP = 1
INDPB = 0.2
# RTS parameter
MAT_POOL_SIZE = 2
REP_POOL_SIZE = 5
evaluator = Evaluator(elapsed_time, simulation_table)
creator.create("FitnessMin", base.Fitness, weights=(-1.0,))
creator.create("Individual", list, fitness=creator.FitnessMin)
toolbox = base.Toolbox()
toolbox.register("attribute", random.random)
toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attribute, n=IND_SIZE)
toolbox.register("population", tools.initRepeat, list, toolbox.individual)
toolbox.register("mate", tools.cxSimulatedBinaryBounded, eta=CXETA, low=CXLOW, up=CXUP)
toolbox.register("mutate", tools.mutPolynomialBounded, eta=MUTETA, low=MUTLOW, up=MUTUP, indpb=INDPB)
toolbox.register("select", tools.selRandom)
toolbox.register("evaluate", evaluator.evaluate)
toolbox.register("test_best_schedule", evaluator.test_best_schedule)
population = toolbox.population(n=POP_SIZE)
fits = toolbox.map(toolbox.evaluate, population)
for fit, ind in zip(fits, population):
ind.fitness.values = fit
for g in range(0, NEVAL, 2):
# Select the next generation individuals
parents = toolbox.select(population, MAT_POOL_SIZE)
# Clone the selected individuals
offsprings = [toolbox.clone(ind) for ind in parents]
# Apply crossover on the offspring
for child1, child2 in zip(offsprings[::2], offsprings[1::2]):
if random.random() < CXPB:
toolbox.mate(child1, child2)
del child1.fitness.values
del child2.fitness.values
# Apply mutation on the offspring
for mutant in offsprings:
if random.random() < MUTPB:
toolbox.mutate(mutant)
del mutant.fitness.values
# Evaluate the individuals with an invalid fitness
for ind in offsprings:
ind.fitness.values = toolbox.evaluate(ind)
# Restricted Tournament Selection
for offspring in offsprings:
replace_pool = toolbox.select(population, REP_POOL_SIZE)
most_similar = findMostSimilarInd(replace_pool, offspring)
if (offspring.fitness.values[0] < most_similar.fitness.values[0]):
population.remove(most_similar)
population.append(offspring)
del replace_pool
del most_similar
del offsprings
del parents
best = None
for pop in population:
if best is None:
best = pop
else:
if pop.fitness.values[0] < best.fitness.values[0]:
best = pop
end_time = time.time()
opt_schedules = list()
evaluator.decode_individual_to_schedules(best, simulation_table, opt_schedules)
optimal_schedules = evaluator.decode_schedules_to_simulation_schedule(opt_schedules)
# optimal_schedule = evaluator.decode_individual_to_schedule(best, simulation_table)
# outFile = open('elapsed_time_for_optimize.txt', 'a')
# outFile.write('Schedule Time: %.05f\n' % (elapsed_time))
# outFile.write('Running Time: %.05f\n' % (end_time - start_time))
# PrintList = ['AHU1', 'AHU2', 'AHU3', 'AHU4', 'AHU5', 'AHU6','AHU7', 'AHU8', 'AHU9', 'AHU10', 'AHU11', 'AHU12']
# valueList = list()
# for i in range(0, EXHIBITION_HALL['number_of_ahu']):
# s = PrintList[i] + ': '
# for j in range(0, int(SIMULATION_TIME_VARIABLE['schedule_horizon_width_in_minutes'] / SIMULATION_TIME_VARIABLE['control_interval_in_minutes'])):
# valueList.append(round(optimal_schedules[j * (EXHIBITION_HALL['number_of_ahu']) + i ], 1))
# s = s + str(valueList)
# outFile.write(s + '\n')
# del valueList[:]
# outFile.close()
test = copy.copy(optimal_schedules)
toolbox.test_best_schedule(test)
# 불 필요한 메모리 삭제
del evaluator
del toolbox
del population
del fits
# del valueList[:]
# del PrintList
# del outFile
return optimal_schedules
