def Main(algorithm, problem, pop_size, crossover_probability,
mutation_probability, n_partitions, n_gen, seed):
# Instancia el problema
problem = Problems.get(problem)
reference_directions = get_reference_directions("das-dennis",
problem.n_obj,
n_partitions=n_partitions)
# Instancia el algoritmo
algorithm = NSGA_II.Get_Algorithm_Instance(
pop_size, crossover_probability, mutation_probability
) if (algorithm == Algorithms.NSGAII) else NSGA_III.Get_Algorithm_Instance(
reference_directions, pop_size, crossover_probability,
mutation_probability) if (algorithm == Algorithms.NSGAIII) else None
# Instancia el optimizador
optimizer = Optimizer(problem, algorithm)
optimization_result = optimizer.Minimize(n_gen, seed)
objective_spaces_values = optimization_result.F
pareto_front = problem.pareto_front(reference_directions) if type(
problem).__name__ == "DTLZ1" else problem.pareto_front()
# Instancia los indicadores de rendimiento (Distancia Generacional Invertida (IGD) / Distancia Generacional Invertida Plus (IGD+))
IGD = get_performance_indicator("igd", pareto_front)
#IGD_plus = get_performance_indicator("igd+", pareto_front)
# Imprime las métricas obtenidas por el conjunto de soluciones resultantes de la optimización multimodal/multiobjetivo
print("IGD:", IGD.calc(objective_spaces_values))
