SPEA2

Example

from jmetal.algorithm.multiobjective.spea2 import SPEA2
from jmetal.operator import SBXCrossover, PolynomialMutation
from jmetal.problem import ZDT1
from jmetal.util.termination_criterion import StoppingByEvaluations

problem = ZDT1()

max_evaluations = 20000

algorithm = SPEA2(
    problem=problem,
    population_size=40,
    offspring_population_size=40,
    mutation=PolynomialMutation(probability=1.0 / problem.number_of_variables, distribution_index=20),
    crossover=SBXCrossover(probability=1.0, distribution_index=20),
    termination_criterion=StoppingByEvaluations(max=max_evaluations)
)

algorithm.run()
solutions = algorithm.get_result()

We can now visualize the Pareto front approximation:

from jmetal.lab.visualization.plotting import Plot
from jmetal.util.solution import get_non_dominated_solutions

front = get_non_dominated_solutions(solutions)

plot_front = Plot(plot_title='Pareto front approximation', axis_labels=['x', 'y'])
plot_front.plot(front, label='SPEA2-ZDT1')

API

class jmetal.algorithm.multiobjective.spea2.SPEA2(problem: jmetal.core.problem.Problem, population_size: int, offspring_population_size: int, mutation: jmetal.core.operator.Mutation, crossover: jmetal.core.operator.Crossover, termination_criterion: jmetal.util.termination_criterion.TerminationCriterion = <jmetal.util.termination_criterion.StoppingByEvaluations object>, population_generator: jmetal.util.generator.Generator = <jmetal.util.generator.RandomGenerator object>, population_evaluator: jmetal.util.evaluator.Evaluator = <jmetal.util.evaluator.SequentialEvaluator object>, dominance_comparator: jmetal.util.comparator.Comparator = <jmetal.util.comparator.DominanceComparator object>)

Bases: jmetal.algorithm.singleobjective.genetic_algorithm.GeneticAlgorithm

get_name() → str

get_result() → R

replacement(population: List[S], offspring_population: List[S]) → List[List[S]]

This method joins the current and offspring populations to produce the population of the next generation by applying the ranking and crowding distance selection.

Parameters

• population – Parent population.

• offspring_population – Offspring population.

Returns

New population after ranking and crowding distance selection is applied.