import random
from copy import copy
from math import sqrt
from typing import TypeVar, List, Optional
import numpy
from jmetal.config import store
from jmetal.core.algorithm import ParticleSwarmOptimization
from jmetal.core.problem import FloatProblem
from jmetal.core.solution import FloatSolution
from jmetal.operator import UniformMutation
from jmetal.operator.mutation import NonUniformMutation
from jmetal.util.archive import BoundedArchive, NonDominatedSolutionsArchive
from jmetal.util.comparator import DominanceComparator, EpsilonDominanceComparator
from jmetal.util.evaluator import Evaluator
from jmetal.util.generator import Generator
from jmetal.util.termination_criterion import TerminationCriterion
R = TypeVar('R')
"""
.. module:: OMOPSO
:platform: Unix, Windows
:synopsis: Implementation of SMPSO.
.. moduleauthor:: Antonio J. Nebro <antonio@lcc.uma.es>
"""
[docs]class OMOPSO(ParticleSwarmOptimization):
def __init__(self,
problem: FloatProblem,
swarm_size: int,
uniform_mutation: UniformMutation,
non_uniform_mutation: NonUniformMutation,
leaders: Optional[BoundedArchive],
epsilon: float,
termination_criterion: TerminationCriterion,
swarm_generator: Generator = store.default_generator,
swarm_evaluator: Evaluator = store.default_evaluator):
""" This class implements the OMOPSO algorithm as described in
todo Update this reference
* SMPSO: A new PSO-based metaheuristic for multi-objective optimization
The implementation of OMOPSO provided in jMetalPy follows the algorithm template described in the algorithm
templates section of the documentation.
:param problem: The problem to solve.
:param swarm_size: Size of the swarm.
:param leaders: Archive for leaders.
"""
super(OMOPSO, self).__init__(
problem=problem,
swarm_size=swarm_size)
self.swarm_generator = swarm_generator
self.swarm_evaluator = swarm_evaluator
self.termination_criterion = termination_criterion
self.observable.register(termination_criterion)
self.uniform_mutation = uniform_mutation
self.non_uniform_mutation = non_uniform_mutation
self.leaders = leaders
self.epsilon = epsilon
self.epsilon_archive = NonDominatedSolutionsArchive(EpsilonDominanceComparator(epsilon))
self.c1_min = 1.5
self.c1_max = 2.0
self.c2_min = 1.5
self.c2_max = 2.0
self.r1_min = 0.0
self.r1_max = 1.0
self.r2_min = 0.0
self.r2_max = 1.0
self.weight_min = 0.1
self.weight_max = 0.5
self.change_velocity1 = -1
self.change_velocity2 = -1
self.dominance_comparator = DominanceComparator()
self.speed = numpy.zeros((self.swarm_size, self.problem.number_of_variables), dtype=float)
[docs] def create_initial_solutions(self) -> List[FloatSolution]:
return [self.swarm_generator.new(self.problem) for _ in range(self.swarm_size)]
[docs] def evaluate(self, solution_list: List[FloatSolution]):
return self.swarm_evaluator.evaluate(solution_list, self.problem)
[docs] def stopping_condition_is_met(self) -> bool:
return self.termination_criterion.is_met
[docs] def initialize_global_best(self, swarm: List[FloatSolution]) -> None:
for particle in swarm:
if self.leaders.add(particle):
self.epsilon_archive.add(copy(particle))
[docs] def initialize_particle_best(self, swarm: List[FloatSolution]) -> None:
for particle in swarm:
particle.attributes['local_best'] = copy(particle)
[docs] def initialize_velocity(self, swarm: List[FloatSolution]) -> None:
for i in range(self.swarm_size):
for j in range(self.problem.number_of_variables):
self.speed[i][j] = 0.0
[docs] def update_velocity(self, swarm: List[FloatSolution]) -> None:
for i in range(self.swarm_size):
best_particle = copy(swarm[i].attributes['local_best'])
best_global = self.select_global_best()
r1 = round(random.uniform(self.r1_min, self.r1_max), 1)
r2 = round(random.uniform(self.r2_min, self.r2_max), 1)
c1 = round(random.uniform(self.c1_min, self.c1_max), 1)
c2 = round(random.uniform(self.c2_min, self.c2_max), 1)
w = round(random.uniform(self.weight_min, self.weight_max), 1)
for var in range(swarm[i].number_of_variables):
self.speed[i][var] = w * self.speed[i][var] \
+ (c1 * r1 * (best_particle.variables[var] - swarm[i].variables[var])) \
+ (c2 * r2 * (best_global.variables[var] - swarm[i].variables[var]))
[docs] def update_position(self, swarm: List[FloatSolution]) -> None:
for i in range(self.swarm_size):
particle = swarm[i]
for j in range(particle.number_of_variables):
particle.variables[j] += self.speed[i][j]
if particle.variables[j] < self.problem.lower_bound[j]:
particle.variables[j] = self.problem.lower_bound[j]
self.speed[i][j] *= self.change_velocity1
if particle.variables[j] > self.problem.upper_bound[j]:
particle.variables[j] = self.problem.upper_bound[j]
self.speed[i][j] *= self.change_velocity2
[docs] def update_global_best(self, swarm: List[FloatSolution]) -> None:
for particle in swarm:
if self.leaders.add(copy(particle)):
self.epsilon_archive.add(copy(particle))
[docs] def update_particle_best(self, swarm: List[FloatSolution]) -> None:
for i in range(self.swarm_size):
flag = self.dominance_comparator.compare(
swarm[i],
swarm[i].attributes['local_best'])
if flag != 1:
swarm[i].attributes['local_best'] = copy(swarm[i])
[docs] def perturbation(self, swarm: List[FloatSolution]) -> None:
self.non_uniform_mutation.set_current_iteration(self.evaluations / self.swarm_size)
for i in range(self.swarm_size):
if (i % 3) == 0:
self.non_uniform_mutation.execute(swarm[i])
else:
self.uniform_mutation.execute(swarm[i])
[docs] def select_global_best(self) -> FloatSolution:
leaders = self.leaders.solution_list
if len(leaders) > 2:
particles = random.sample(leaders, 2)
if self.leaders.comparator.compare(particles[0], particles[1]) < 1:
best_global = copy(particles[0])
else:
best_global = copy(particles[1])
else:
best_global = copy(self.leaders.solution_list[0])
return best_global
def __velocity_constriction(self, value: float, delta_max: [], delta_min: [], variable_index: int) -> float:
result = value
if value > delta_max[variable_index]:
result = delta_max[variable_index]
if value < delta_min[variable_index]:
result = delta_min[variable_index]
return result
def __inertia_weight(self, wmax: float):
return wmax
def __constriction_coefficient(self, c1: float, c2: float) -> float:
rho = c1 + c2
if rho <= 4:
result = 1.0
else:
result = 2.0 / (2.0 - rho - sqrt(pow(rho, 2.0) - 4.0 * rho))
return result
[docs] def init_progress(self) -> None:
self.evaluations = self.swarm_size
self.leaders.compute_density_estimator()
self.initialize_velocity(self.solutions)
self.initialize_particle_best(self.solutions)
self.initialize_global_best(self.solutions)
[docs] def update_progress(self) -> None:
self.evaluations += self.swarm_size
self.leaders.compute_density_estimator()
observable_data = self.get_observable_data()
observable_data['SOLUTIONS'] = self.epsilon_archive.solution_list
self.observable.notify_all(**observable_data)
[docs] def get_result(self) -> List[FloatSolution]:
return self.epsilon_archive.solution_list
[docs] def get_name(self) -> str:
return 'OMOPSO'
