s_ranjbar/energy_system_modelling_workflow
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fix: small changes in NSGA-II

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This commit is contained in:
Saeed Ranjbar 2024-11-13 14:15:22 +01:00
parent 7944a36dbf
commit 09d1d08ed7
2 changed files with 3 additions and 4 deletions
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5
energy_system_modelling_package/energy_system_modelling_factories/system_sizing_methods/genetic_algorithm/multi_objective_genetic_algorithm.py
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@ -38,7 +38,7 @@ class MultiObjectiveGeneticAlgorithm:
operators such as crossover and mutation rates.
"""
def __init__(self, population_size=20, generations=20, crossover_rate=0.9, mutation_rate=0.33,
def __init__(self, population_size=100, generations=100, crossover_rate=0.9, mutation_rate=0.1,
number_of_selected_solutions=None, optimization_scenario=None):
self.population_size = population_size
self.population = []
@ -441,13 +441,11 @@ class MultiObjectiveGeneticAlgorithm:
return new_population
def solve_ga(self, building, energy_system):
# df = pd.DataFrame()
self.initialize_population(building, energy_system)
for n in range(self.generations + 1):
print(n)
progeny_population = []
while len(progeny_population) < self.population_size:
parent1, parent2 = random.choice(self.population), random.choice(self.population)
child1, child2 = self.sbx_crossover(parent1, parent2)
self.polynomial_mutation(child1.individual, building, energy_system)
@ -457,6 +455,7 @@ class MultiObjectiveGeneticAlgorithm:
progeny_population.extend([child1, child2])
self.population.extend(progeny_population)
fronts = self.fast_non_dominated_sorting()
print(fronts)
crowding_distances = [0] * len(self.population)
for front in fronts:
self.calculate_crowding_distance(front=front, crowding_distance=crowding_distances)

2
test.py
View File

@ -54,5 +54,5 @@ energy_plus_workflow(city, energy_plus_output_path)
random_assignation.call_random(city.buildings, random_assignation.residential_new_systems_percentage)
EnergySystemsFactory('montreal_future', city).enrich()
for building in city.buildings:
energy_system = building.energy_systems[-1]
energy_system = building.energy_systems[1]
MultiObjectiveGeneticAlgorithm(optimization_scenario='energy-consumption_cost').solve_ga(building, energy_system)