front = get_non_dominated_solutions(final) #process[-1]
process.append(front)
smrprocessruns.append(process)
reference += front
reference_front = get_non_dominated_solutions(reference)
plot_front = Plot(plot_title='Pareto front approximation',
axis_labels=['TRT', 'TDC'],
reference_front=None)
plot_front.plot(reference_front, label=' ', filename=fm, format='png')
# Save results to file
print_function_values_to_file(
reference_front, 'FUN.' + algorithm.get_name() + "-" + problem.get_name())
print_variables_to_file(
reference_front, 'VAR.' + algorithm.get_name() + "-" + problem.get_name())
# Normalization
timelist = []
costlist = []
for s in reference_front:
timelist.append(s.objectives[0])
costlist.append(s.objectives[1])
mintime = min(timelist)
maxtime = max(timelist)
mincost = min(costlist)
maxcost = max(costlist)
print("Time:", mintime, maxtime)
print("Cost:", mincost, maxcost)
from jmetal.util.termination_criterion import StoppingByEvaluations
if __name__ == '__main__':
problem = TSP(instance='../../resources/TSP_instances/kroA100.tsp')
print('Cities: ', problem.number_of_variables)
algorithm = GeneticAlgorithm(
problem=problem,
population_size=100,
offspring_population_size=100,
mutation=PermutationSwapMutation(1.0 / problem.number_of_variables),
crossover=PMXCrossover(0.8),
selection=BinaryTournamentSelection(
MultiComparator([
FastNonDominatedRanking.get_comparator(),
CrowdingDistance.get_comparator()
])),
termination_criterion=StoppingByEvaluations(max=2500000))
algorithm.observable.register(observer=PrintObjectivesObserver(1000))
algorithm.run()
result = algorithm.get_result()
print('Algorithm: {}'.format(algorithm.get_name()))
print('Problem: {}'.format(problem.get_name()))
print('Solution: {}'.format(result.variables))
print('Fitness: {}'.format(result.objectives[0]))
print('Computing time: {}'.format(algorithm.total_computing_time))
from jmetal.algorithm.singleobjective.genetic_algorithm import GeneticAlgorithm
from jmetal.operator import BinaryTournamentSelection, BitFlipMutation, SPXCrossover
from jmetal.problem.singleobjective.knapsack import Knapsack
from jmetal.util.termination_criterion import StoppingByEvaluations
if __name__ == "__main__":
problem = Knapsack(
from_file=True,
filename="resources/Knapsack_instances/KnapsackInstance_50_0_0.kp")
algorithm = GeneticAlgorithm(
problem=problem,
population_size=100,
offspring_population_size=1,
mutation=BitFlipMutation(probability=0.1),
crossover=SPXCrossover(probability=0.8),
selection=BinaryTournamentSelection(),
termination_criterion=StoppingByEvaluations(max_evaluations=25000),
)
algorithm.run()
subset = algorithm.get_result()
print("Algorithm: {}".format(algorithm.get_name()))
print("Problem: {}".format(problem.get_name()))
print("Solution: {}".format(subset.variables))
print("Fitness: {}".format(-subset.objectives[0]))
print("Computing time: {}".format(algorithm.total_computing_time))
print(f"Problem Maximum Capacity: {problem.capacity}")