"""
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))