OMOPSO

Example

from jmetal.algorithm.multiobjective.omopso import OMOPSO
from jmetal.operator.mutation import UniformMutation
from jmetal.operator.mutation import NonUniformMutation
from jmetal.problem import ZDT1
from jmetal.util.archive import CrowdingDistanceArchive
from jmetal.util.termination_criterion import StoppingByEvaluations

problem = ZDT1()

mutation_probability = 1.0 / problem.number_of_variables
max_evaluations = 25000
swarm_size = 100

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=int(max_evaluations / swarm_size)),
    leaders=CrowdingDistanceArchive(100),
    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='OMOPSO-ZDT1')

API

class jmetal.algorithm.multiobjective.omopso.OMOPSO(problem: ~jmetal.core.problem.FloatProblem, swarm_size: int, uniform_mutation: ~jmetal.operator.mutation.UniformMutation, non_uniform_mutation: ~jmetal.operator.mutation.NonUniformMutation, leaders: ~jmetal.util.archive.BoundedArchive | None, epsilon: float, termination_criterion: ~jmetal.util.termination_criterion.TerminationCriterion, swarm_generator: ~jmetal.util.generator.Generator = <jmetal.util.generator.RandomGenerator object>, swarm_evaluator: ~jmetal.util.evaluator.Evaluator = <jmetal.util.evaluator.SequentialEvaluator object>)

Bases: ParticleSwarmOptimization

create_initial_solutions() → List[FloatSolution]

Creates the initial list of solutions of a metaheuristic.

evaluate(solution_list: List[FloatSolution])

Evaluates a solution list.

get_name() → str

init_progress() → None

Initialize the algorithm.

initialize_global_best(swarm: List[FloatSolution]) → None

initialize_particle_best(swarm: List[FloatSolution]) → None

initialize_velocity(swarm: List[FloatSolution]) → None

perturbation(swarm: List[FloatSolution]) → None

result() → List[FloatSolution]

select_global_best() → FloatSolution

stopping_condition_is_met() → bool

The stopping condition is met or not.

update_global_best(swarm: List[FloatSolution]) → None

update_particle_best(swarm: List[FloatSolution]) → None

update_position(swarm: List[FloatSolution]) → None

update_progress() → None

Update the progress after each iteration.

update_velocity(swarm: List[FloatSolution]) → None