""" if __name__ == "__main__": problem = ZDT1Modified() max_evaluations = 100 algorithm = NSGAII( problem=problem, population_size=10, offspring_population_size=10, mutation=PolynomialMutation(probability=1.0 / problem.number_of_variables, distribution_index=20), crossover=SBXCrossover(probability=1.0, distribution_index=20), population_evaluator=SparkEvaluator(), termination_criterion=StoppingByEvaluations( max_evaluations=max_evaluations), ) algorithm.run() front = algorithm.get_result() # Save results to file print_function_values_to_file(front, "FUN." + algorithm.label) print_variables_to_file(front, "VAR." + algorithm.label) print(f"Algorithm: {algorithm.get_name()}") print(f"Problem: {problem.get_name()}") print(f"Computing time: {algorithm.total_computing_time}")
if __name__ == '__main__': problem = ZDT1Modified() problem.reference_front = read_solutions(filename='resources/reference_front/ZDT1.pf') mutation_probability = 1.0 / problem.number_of_variables max_evaluations = 100 swarm_size = 10 algorithm = OMOPSO( problem=problem, swarm_size=swarm_size, epsilon=0.0075, uniform_mutation=UniformMutation(probability=mutation_probability, perturbation=0.5), non_uniform_mutation=NonUniformMutation(mutation_probability, perturbation=0.5, max_iterations=max_evaluations / swarm_size), leaders=CrowdingDistanceArchive(10), termination_criterion=StoppingByEvaluations(max_evaluations=max_evaluations), swarm_evaluator=SparkEvaluator(), ) algorithm.run() front = algorithm.get_result() # Save results to file print_function_values_to_file(front, 'FUN.' + algorithm.get_name() + "." + problem.get_name()) print_variables_to_file(front, 'VAR.' + algorithm.get_name() + "." + problem.get_name()) print('Algorithm (continuous problem): ' + algorithm.get_name()) print('Problem: ' + problem.get_name()) print('Computing time: ' + str(algorithm.total_computing_time))