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)
def configurar_SPEA2(self):
algorithm = SPEA2(
problem=self.problema,
population_size=self.maxima_poblacion,
offspring_population_size=self.maxima_poblacion,
mutation=PolynomialMutation(probability=self.probabilidad, distribution_index=0.20),
crossover=SBXCrossover(probability=self.probabilidad, distribution_index=20),
termination_criterion=StoppingByEvaluations(max_evaluations=self.evaluaciones),
dominance_comparator=DominanceComparator())
return algorithm
def __init__(self,
problem: Problem[S],
mutation: Mutation,
termination_criterion: TerminationCriterion,
comparator=DominanceComparator()):
super(LocalSearch, self).__init__()
self.comparator = comparator
self.problem = problem
self.mutation = mutation
self.termination_criterion = termination_criterion
self.observable.register(termination_criterion)
def __init__(
self,
problem: FloatProblem,
swarm_size: int,
mutation: Mutation,
leaders: Optional[BoundedArchive],
termination_criterion: TerminationCriterion = store.
default_termination_criteria,
swarm_generator: Generator = store.default_generator,
swarm_evaluator: Evaluator = store.default_evaluator,
):
"""This class implements the SMPSO algorithm as described in
* SMPSO: A new PSO-based metaheuristic for multi-objective optimization
* MCDM 2009. DOI: `<http://dx.doi.org/10.1109/MCDM.2009.4938830/>`_.
The implementation of SMPSO 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 max_evaluations: Maximum number of evaluations/iterations.
:param mutation: Mutation operator (see :py:mod:`jmetal.operator.mutation`).
:param leaders: Archive for leaders.
"""
super(SMPSO, 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.mutation_operator = mutation
self.leaders = leaders
self.c1_min = 1.5
self.c1_max = 2.5
self.c2_min = 1.5
self.c2_max = 2.5
self.r1_min = 0.0
self.r1_max = 1.0
self.r2_min = 0.0
self.r2_max = 1.0
self.min_weight = 0.1
self.max_weight = 0.1
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)
self.delta_max, self.delta_min = (
numpy.empty(problem.number_of_variables),
numpy.empty(problem.number_of_variables),
)
def configurar_NSGAIII(self):
algorithm = NSGAIII(
problem=self.problema,
reference_directions = UniformReferenceDirectionFactory(5, n_points=91),
population_size=self.maxima_poblacion,
mutation=PolynomialMutation(probability = self.probabilidad , distribution_index=0.20),
crossover=SBXCrossover(probability= self.probabilidad, distribution_index=20),
termination_criterion=StoppingByEvaluations(max_evaluations=self.evaluaciones),
dominance_comparator=DominanceComparator())
return algorithm
def execute(self, solution_list: List[S]) -> S:
if solution_list is None:
raise Exception("The solution list is null")
elif len(solution_list) == 0:
raise Exception("The solution is empty")
result = solution_list[0]
for solution in solution_list[1:]:
if DominanceComparator().compare(solution, result) < 0:
result = solution
return result
def execute(self, individuals: List[Solution]) -> List[Solution]:
assert len(individuals) == 2
sol_a, sol_b = individuals
better_sol, worse_sol = sol_a, sol_b
if DominanceComparator().compare(sol_a, sol_b):
better_sol, worse_sol = sol_b, sol_a
worse_sol.transfer_energy_to(better_sol,
self.exchange_factor * worse_sol.energy)
return [sol_a, sol_b]
def execute(self, front: List[S]) -> S:
if front is None:
raise Exception('The front is null')
elif len(front) == 0:
raise Exception('The front is empty')
result = front[0]
for solution in front[1:]:
if DominanceComparator().compare(solution, result) < 0:
result = solution
return result
def __init__(self,
problem: DynamicProblem,
population_size: int,
cr: float,
f: float,
termination_criterion: TerminationCriterion,
k: float = 0.5,
population_generator: Generator = store.default_generator,
population_evaluator: Evaluator = store.default_evaluator,
dominance_comparator: Comparator = DominanceComparator()):
super(DynamicGDE3, self).__init__(
problem, population_size, cr, f, termination_criterion, k,
population_generator, population_evaluator, dominance_comparator)
self.completed_iterations = 0
def compute_ranking(self, solution_list: List[S]):
# number of solutions dominating solution ith
dominate_me = [0 for i in range(len(solution_list))]
# list of solutions dominated by solution ith
i_dominate = [[] for i in range(len(solution_list))]
# front[i] contains the list of solutions belonging to front i
front = [[] for i in range(len(solution_list) + 1)]
for p in range(len(solution_list) - 1):
for q in range(p + 1, len(solution_list)):
dominance_test_result = DominanceComparator().compare(
solution_list[p], solution_list[q])
if dominance_test_result is -1:
i_dominate[p].append(q)
dominate_me[q] += 1
elif dominance_test_result is 1:
i_dominate[q].append(p)
dominate_me[p] += 1
for i in range(len(solution_list)):
if dominate_me[i] is 0:
front[0].append(i)
solution_list[i].attributes["dominance_ranking"] = 0
i = 0
while len(front[i]) != 0:
i += 1
for p in front[i - 1]:
if p <= len(i_dominate):
for q in i_dominate[p]:
index = q
dominate_me[index] -= 1
if dominate_me[index] is 0:
front[i].append(index)
solution_list[index].attributes[
"dominance_ranking"] = i
self.ranked_sublists = [[]] * i
for j in range(i):
Q = [0] * len(front[j])
for k in range(len(front[j])):
Q[k] = solution_list[front[j][k]]
self.ranked_sublists[j] = Q
return self.ranked_sublists
def __init__(self,
problem: FloatProblem,
swarm_size: int,
max_evaluations: int,
mutation: Mutation[FloatSolution],
leaders: BoundedArchive[FloatSolution],
observable: Observable = DefaultObservable(),
evaluator: Evaluator[FloatSolution] = SequentialEvaluator[
FloatSolution]()):
super(SMPSO, self).__init__()
self.problem = problem
self.swarm_size = swarm_size
self.max_evaluations = max_evaluations
self.mutation: Mutation[FloatSolution] = mutation
self.leaders = leaders
self.observable = observable
self.evaluator = evaluator
self.evaluations = 0
self.c1_min = 1.5
self.c1_max = 2.5
self.c2_min = 1.5
self.c2_max = 2.5
self.min_weight = 0.1
self.max_weight = 0.1
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)
self.delta_max = numpy.empty(problem.number_of_variables)
self.delta_min = numpy.empty(problem.number_of_variables)
for i in range(problem.number_of_variables):
self.delta_max[i] = (self.problem.upper_bound[i] -
self.problem.lower_bound[i]) / 2.0
self.delta_min = -1.0 * self.delta_max
def __init__(self,
problem: Problem,
population_size: int,
offspring_population_size: int,
mutation: Mutation,
crossover: Crossover,
selection: Selection,
termination_criterion: TerminationCriterion,
population_generator: Generator = store.default_generator,
population_evaluator: Evaluator = store.default_evaluator,
dominance_comparator: Comparator = DominanceComparator()):
""" NSGA-II implementation as described in
* K. Deb, A. Pratap, S. Agarwal and T. Meyarivan, "A fast and elitist
multiobjective genetic algorithm: NSGA-II," in IEEE Transactions on Evolutionary Computation,
vol. 6, no. 2, pp. 182-197, Apr 2002. doi: 10.1109/4235.996017
NSGA-II is a genetic algorithm (GA), i.e. it belongs to the evolutionary algorithms (EAs)
family. The implementation of NSGA-II provided in jMetalPy follows the evolutionary
algorithm template described in the algorithm module (:py:mod:`jmetal.core.algorithm`).
.. note:: A steady-state version of this algorithm can be run by setting the offspring size to 1 and the mating pool size to 2.
:param problem: The problem to solve.
:param population_size: Size of the population.
:param mutation: Mutation operator (see :py:mod:`jmetal.operator.mutation`).
:param crossover: Crossover operator (see :py:mod:`jmetal.operator.crossover`).
:param selection: Selection operator (see :py:mod:`jmetal.operator.selection`).
"""
super(NSGAII, self).__init__(
problem=problem,
population_size=population_size,
offspring_population_size=offspring_population_size,
mutation=mutation,
crossover=crossover,
selection=selection,
termination_criterion=termination_criterion,
population_evaluator=population_evaluator,
population_generator=population_generator)
self.dominance_comparator = dominance_comparator
def __init__(self,
problem: Problem,
population_size: int,
cr: float,
f: float,
termination_criterion: TerminationCriterion,
k: float = 0.5,
population_generator: Generator = store.default_generator,
population_evaluator: Evaluator = store.default_evaluator,
dominance_comparator: Comparator = DominanceComparator()):
super(GDE3, self).__init__(problem=problem,
population_size=population_size,
offspring_population_size=population_size)
self.dominance_comparator = dominance_comparator
self.selection_operator = DifferentialEvolutionSelection()
self.crossover_operator = DifferentialEvolutionCrossover(cr, f, k)
self.population_generator = population_generator
self.population_evaluator = population_evaluator
self.termination_criterion = termination_criterion
self.observable.register(termination_criterion)
if __name__ == "__main__":
problem = Srinivas()
problem.reference_front = read_solutions(
filename="resources/reference_front/Srinivas.pf")
max_evaluations = 25000
algorithm = NSGAII(
problem=problem,
population_size=100,
offspring_population_size=100,
mutation=PolynomialMutation(probability=1.0 /
problem.number_of_variables,
distribution_index=20),
crossover=SBXCrossover(probability=1.0, distribution_index=20),
termination_criterion=StoppingByEvaluations(
max_evaluations=max_evaluations),
dominance_comparator=DominanceComparator(),
)
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}")
def __init__(self, comparator: Comparator = DominanceComparator()):
super(BinaryTournamentSelection, self).__init__()
self.comparator = comparator
def __init__(self, max_population_size: int, dominance_comparator: Comparator = DominanceComparator()):
super(RankingAndCrowdingDistanceSelection, self).__init__()
self.max_population_size = max_population_size
self.dominance_comparator = dominance_comparator
def setUp(self):
self.comparator = DominanceComparator()
def __init__(self, comparator: Comparator = DominanceComparator()):
super(StrengthRanking, self).__init__(comparator)
def __init__(self, comparator: Comparator = DominanceComparator()):
super(FastNonDominatedRanking, self).__init__(comparator)
def default_comparator(self):
return DominanceComparator()
def __init__(self, comparator: Comparator = DominanceComparator()):
super(Ranking, self).__init__()
self.number_of_comparisons = 0
self.ranked_sublists = []
self.comparator = comparator
def run(self) -> List[S]:
pool_1_size = self.population_size
pool_2_size = self.population_size
selection_operator_1 = BinaryTournamentSelection()
crossover_operator_1 = IntegerSBXCrossover(1.0, 20.0)
mutation_operator_1 = IntegerPolynomialMutation(
1.0 / self.problem.number_of_variables, 20.0)
selection_operator_2 = DifferentialEvolutionSelection()
crossover_operator_2 = DifferentialEvolutionCrossover(0.2, 0.5, 0.5)
dominance = DominanceComparator()
max_iterations = self.max_iterations
iterations = 0
parent_1: List[IntegerSolution] = [None, None]
generational_hv: List[float] = []
current_gen = 0
"""Create the initial subpopulation pools and evaluate them"""
pool_1: List[IntegerSolution] = []
for i in range(pool_1_size):
pool_1.append(self.problem.create_solution())
pool_1[i] = self.problem.evaluate(pool_1[i])
pool_2: List[IntegerSolution] = []
for i in range(pool_2_size):
pool_2.append(self.problem.create_solution())
pool_2[i] = self.problem.evaluate(pool_2[i])
evaluations = pool_1_size + pool_2_size
mix = self.mix_interval
problem = self.problem
h = HyperVolume(reference_point=[1] *
self.problem.number_of_objectives)
initial_population = True
"""The main evolutionary cycle"""
while iterations < max_iterations:
combi: List[IntegerSolution] = []
if not initial_population:
offspring_pop_1: List[IntegerSolution] = []
offspring_pop_2: List[IntegerSolution] = []
"""Evolve pool 1"""
for i in range(pool_1_size):
parent_1[0] = selection_operator_1.execute(pool_1)
parent_1[1] = selection_operator_1.execute(pool_1)
child_1: IntegerSolution = crossover_operator_1.execute(
parent_1)[0]
child_1 = mutation_operator_1.execute(child_1)
child_1 = problem.evaluate(child_1)
evaluations += 1
offspring_pop_1.append(child_1)
"""Evolve pool 2"""
for i in range(pool_2_size):
parent_2: List[
IntegerSolution] = selection_operator_2.execute(pool_2)
crossover_operator_2.current_individual = pool_2[i]
child_2 = crossover_operator_2.execute(parent_2)
child_2 = problem.evaluate(child_2[0])
evaluations += 1
result = dominance.compare(pool_2[i], child_2)
if result == -1:
offspring_pop_2.append(pool_2[i])
elif result == 1:
offspring_pop_2.append(child_2)
else:
offspring_pop_2.append(child_2)
offspring_pop_2.append(pool_2[i])
ind_1 = pool_1[random.randint(0, pool_1_size - 1)]
ind_2 = pool_2[random.randint(0, pool_2_size - 1)]
offspring_pop_1.append(ind_1)
offspring_pop_2.append(ind_2)
offspring_pop_1.extend(pool_1)
pool_1 = self.r.replace(offspring_pop_1[:pool_1_size],
offspring_pop_1[pool_1_size:])
pool_2 = self.r.replace(offspring_pop_2[:pool_2_size],
offspring_pop_2[pool_2_size:])
mix -= 1
if mix == 0:
"""Time to perform fitness sharing"""
mix = self.mix_interval
combi = combi + pool_1 + pool_2
# print("Combi size: ", len(combi))
"""pool1size/10"""
combi = self.r.replace(
combi[:int(pool_1_size / 10)],
combi[int(pool_1_size / 10):len(combi)],
)
"""
print(
"Sizes: ",
len(pool_1) + len(combi),
len(pool_2) + len(combi),
"\n",
)
"""
pool_1 = self.r.replace(pool_1, combi)
pool_2 = self.r.replace(pool_2, combi)
if initial_population:
initial_population = False
iterations += 1
print("Iterations: ", str(iterations))
"""
hval_1 = h.compute([s.objectives for s in pool_1])
hval_2 = h.compute([s.objectives for s in pool_2])
print("hval_1: ", str(hval_1))
print("hval_2: ", str(hval_2), "\n")
"""
new_gen = int(evaluations / self.report_interval)
if new_gen > current_gen:
combi = combi + pool_1 + pool_2
combi = self.r.replace(combi[:(2 * pool_1_size)],
combi[(2 * pool_1_size):])
hval = h.compute([s.objectives for s in combi])
for i in range(current_gen, new_gen, 1):
generational_hv.append(hval)
current_gen = new_gen
"""#Write runtime generational HV to file"""
"""Return the first non dominated front"""
combi_ini: List[IntegerSolution] = []
combi_ini.extend(pool_1)
combi_ini.extend(pool_2)
combi_ini = self.r.replace(
combi_ini[:pool_1_size + pool_2_size],
combi_ini[pool_1_size + pool_2_size:],
)
return combi_ini
def __init__(self):
super(NonDominatedSolutionListArchive, self).__init__()
self.comparator = DominanceComparator()
def __init__(self, dominance_comparator: Comparator = DominanceComparator()):
super(NonDominatedSolutionsArchive, self).__init__()
self.comparator = dominance_comparator
def run(self) -> List[S]:
# selection operator 1
selection_operator_1 = BinaryTournamentSelection()
# selection operator 2
selection_operator_2 = DifferentialEvolutionSelection()
# crossover operator 1
crossover_operator_1 = SBXCrossover(1.0, 20.0)
# crossover operator 2
crossover_operator_2 = DifferentialEvolutionCrossover(0.2, 0.5, 0.5)
# crossover operator 3
crossover_operator_3 = DifferentialEvolutionCrossover(1.0, 0.5, 0.5)
# mutation operator 1
mutation_operator_1 = PolynomialMutation(
1.0 / self.problem.number_of_variables, 20.0)
# dominance comparator
dominance = DominanceComparator()
# array that stores the "generational" HV quality
generational_hv: List[float] = []
parent_1: List[FloatSolution] = [None, None]
parent_2: List[FloatSolution] = []
parent_3: List[FloatSolution] = []
# initialize some local and global variables
pool_1: List[FloatSolution] = []
pool_2: List[FloatSolution] = []
# size of elite subset used for fitness sharing between subpopulations
nrOfDirectionalSolutionsToEvolve = int(self.population_size / 5)
# subpopulation 1
pool_1_size = int(self.population_size -
(nrOfDirectionalSolutionsToEvolve / 2))
# subpopulation 2
pool_2_size = int(self.population_size -
(nrOfDirectionalSolutionsToEvolve / 2))
print(
str(pool_1_size) + " - " + str(nrOfDirectionalSolutionsToEvolve) +
" - " + str(self.mix_interval))
evaluations = 0
current_gen = 0
directionalArchiveSize = 2 * self.population_size
weights = self.__create_uniform_weights(
directionalArchiveSize, self.problem.number_of_objectives)
directionalArchive = self.__create_directional_archive(weights)
neighbourhoods = self.__create_neighbourhoods(directionalArchive,
self.population_size)
nrOfReplacements = 1
iniID = 0
# Create the initial pools
# pool1
pool_1: List[FloatSolution] = []
for _ in range(pool_1_size):
new_solution = self.problem.create_solution()
new_solution = self.problem.evaluate(new_solution)
evaluations += 1
pool_1.append(new_solution)
self.__update_extreme_values(new_solution)
dr = directionalArchive[iniID]
dr.curr_sol = new_solution
iniID += 1
# pool2
pool_2: List[FloatSolution] = []
for _ in range(pool_2_size):
new_solution = self.problem.create_solution()
new_solution = self.problem.evaluate(new_solution)
evaluations += 1
pool_2.append(new_solution)
self.__update_extreme_values(new_solution)
dr = directionalArchive[iniID]
dr.curr_sol = new_solution
iniID += 1
# directional archive initialization
pool_A: List[FloatSolution] = []
while iniID < directionalArchiveSize:
new_solution = self.problem.create_solution()
new_solution = self.problem.evaluate(new_solution)
evaluations += 1
pool_A.append(new_solution)
self.__update_extreme_values(new_solution)
dr = directionalArchive[iniID]
dr.curr_sol = new_solution
iniID += 1
mix = self.mix_interval
h = HyperVolume(reference_point=[1] *
self.problem.number_of_objectives)
insertionRate: List[float] = [0, 0, 0]
bonusEvals: List[int] = [0, 0, nrOfDirectionalSolutionsToEvolve]
testRun = True
# record the generational HV of the initial population
combiAll: List[FloatSolution] = []
cGen = int(evaluations / self.report_interval)
if cGen > 0:
combiAll = pool_1 + pool_2 + pool_A
combiAll = self.r.replace(
combiAll[:pool_1_size + pool_2_size],
combiAll[pool_1_size + pool_2_size:],
)
hval = h.compute([s.objectives for s in combiAll])
for _ in range(cGen):
generational_hv.append(hval)
current_gen = cGen
# the main loop of the algorithm
while evaluations < self.max_evaluations:
offspringPop1: List[FloatSolution] = []
offspringPop2: List[FloatSolution] = []
offspringPop3: List[FloatSolution] = []
dirInsertPool1: List[FloatSolution] = []
dirInsertPool2: List[FloatSolution] = []
dirInsertPool3: List[FloatSolution] = []
# evolve pool1 - using SPEA2 evolutionary model
nfe: int = 0
while nfe < (pool_1_size + bonusEvals[0]):
parent_1[0] = selection_operator_1.execute(pool_1)
parent_1[1] = selection_operator_1.execute(pool_1)
child1a: FloatSolution = crossover_operator_1.execute(
parent_1)[0]
child1a = mutation_operator_1.execute(child1a)
child1a = self.problem.evaluate(child1a)
evaluations += 1
nfe += 1
offspringPop1.append(child1a)
dirInsertPool1.append(child1a)
# evolve pool2 - using DEMO SP evolutionary model
i: int = 0
unselectedIDs: List[int] = []
for ID in range(len(pool_2)):
unselectedIDs.append(ID)
nfe = 0
while nfe < (pool_2_size + bonusEvals[1]):
index = random.randint(0, len(unselectedIDs) - 1)
i = unselectedIDs[index]
unselectedIDs.pop(index)
parent_2 = selection_operator_2.execute(pool_2)
crossover_operator_2.current_individual = pool_2[i]
child2 = crossover_operator_2.execute(parent_2)
child2 = self.problem.evaluate(child2[0])
evaluations += 1
nfe += 1
result = dominance.compare(pool_2[i], child2)
if result == -1: # solution i dominates child
offspringPop2.append(pool_2[i])
elif result == 1: # child dominates
offspringPop2.append(child2)
else: # the two solutions are non-dominated
offspringPop2.append(child2)
offspringPop2.append(pool_2[i])
dirInsertPool2.append(child2)
if len(unselectedIDs) == 0:
for ID in range(len(pool_2)):
unselectedIDs.append(random.randint(
0,
len(pool_2) - 1))
# evolve pool3 - Directional Decomposition DE/rand/1/bin
IDs = self.__compute_neighbourhood_Nfe_since_last_update(
neighbourhoods, directionalArchive,
nrOfDirectionalSolutionsToEvolve)
nfe = 0
for j in range(len(IDs)):
if nfe < bonusEvals[2]:
nfe += 1
else:
break
cID = IDs[j]
chosenSol: FloatSolution = None
if directionalArchive[cID].curr_sol != None:
chosenSol = directionalArchive[cID].curr_sol
else:
chosenSol = pool_1[0]
print("error!")
parent_3: List[FloatSolution] = [None, None, None]
r1 = random.randint(0, len(neighbourhoods[cID]) - 1)
r2 = random.randint(0, len(neighbourhoods[cID]) - 1)
r3 = random.randint(0, len(neighbourhoods[cID]) - 1)
while r2 == r1:
r2 = random.randint(0, len(neighbourhoods[cID]) - 1)
while r3 == r1 or r3 == r2:
r3 = random.randint(0, len(neighbourhoods[cID]) - 1)
parent_3[0] = directionalArchive[r1].curr_sol
parent_3[1] = directionalArchive[r2].curr_sol
parent_3[2] = directionalArchive[r3].curr_sol
crossover_operator_3.current_individual = chosenSol
child3 = crossover_operator_3.execute(parent_3)[0]
child3 = mutation_operator_1.execute(child3)
child3 = self.problem.evaluate(child3)
evaluations += 1
dirInsertPool3.append(child3)
# compute directional improvements
# pool1
improvements = 0
for j in range(len(dirInsertPool1)):
testSol = dirInsertPool1[j]
self.__update_extreme_values(testSol)
improvements += self.__update_neighbourhoods(
directionalArchive, testSol, nrOfReplacements)
insertionRate[0] += (1.0 * improvements) / len(dirInsertPool1)
# pool2
improvements = 0
for j in range(len(dirInsertPool2)):
testSol = dirInsertPool2[j]
self.__update_extreme_values(testSol)
improvements += self.__update_neighbourhoods(
directionalArchive, testSol, nrOfReplacements)
insertionRate[1] += (1.0 * improvements) / len(dirInsertPool2)
# pool3
improvements = 0
for j in range(len(dirInsertPool3)):
testSol = dirInsertPool3[j]
self.__update_extreme_values(testSol)
improvements += self.__update_neighbourhoods(
directionalArchive, testSol, nrOfReplacements)
# on java, dividing a floating number by 0, returns NaN
# on python, dividing a floating number by 0, returns an exception
if len(dirInsertPool3) == 0:
insertionRate[2] = None
else:
insertionRate[2] += (1.0 * improvements) / len(dirInsertPool3)
for dr in directionalArchive:
offspringPop3.append(dr.curr_sol)
offspringPop1 = offspringPop1 + pool_1
pool_1 = self.r.replace(offspringPop1[:pool_1_size],
offspringPop1[pool_1_size:])
pool_2 = self.r.replace(offspringPop2[:pool_2_size],
offspringPop2[pool_2_size:])
combi: List[FloatSolution] = []
mix -= 1
if mix == 0:
mix = self.mix_interval
combi = combi + pool_1 + pool_2 + offspringPop3
print("Combi size: " + str(len(combi)))
combi = self.r.replace(
combi[:nrOfDirectionalSolutionsToEvolve],
combi[nrOfDirectionalSolutionsToEvolve:],
)
insertionRate[0] /= self.mix_interval
insertionRate[1] /= self.mix_interval
if insertionRate[2] != None:
insertionRate[2] /= self.mix_interval
"""
print(
"Insertion rates: "
+ str(insertionRate[0])
+ " - "
+ str(insertionRate[1])
+ " - "
+ str(insertionRate[2])
+ " - Test run:"
+ str(testRun)
)
"""
if testRun:
if (insertionRate[0] > insertionRate[1]) and (
insertionRate[0] > insertionRate[2]):
print("SPEA2 win - bonus run!")
bonusEvals[0] = nrOfDirectionalSolutionsToEvolve
bonusEvals[1] = 0
bonusEvals[2] = 0
if (insertionRate[1] > insertionRate[0]) and (
insertionRate[1] > insertionRate[2]):
print("DE win - bonus run!")
bonusEvals[0] = 0
bonusEvals[1] = nrOfDirectionalSolutionsToEvolve
bonusEvals[2] = 0
if (insertionRate[2] > insertionRate[0]) and (
insertionRate[2] > insertionRate[1]):
print("Directional win - no bonus!")
bonusEvals[0] = 0
bonusEvals[1] = 0
bonusEvals[2] = nrOfDirectionalSolutionsToEvolve
else:
print("Test run - no bonus!")
bonusEvals[0] = 0
bonusEvals[1] = 0
bonusEvals[2] = nrOfDirectionalSolutionsToEvolve
testRun = not testRun
insertionRate[0] = 0.0
insertionRate[1] = 0.0
insertionRate[2] = 0.0
pool_1 = pool_1 + combi
pool_2 = pool_2 + combi
print("Sizes: " + str(len(pool_1)) + " " + str(len(pool_2)))
pool_1 = self.r.replace(pool_1[:pool_1_size],
pool_1[pool_1_size:])
pool_2 = self.r.replace(pool_2[:pool_2_size],
pool_2[pool_2_size:])
self.__clear_Nfe_history(directionalArchive)
hVal1 = h.compute([s.objectives for s in pool_1])
hVal2 = h.compute([s.objectives for s in pool_2])
hVal3 = h.compute([s.objectives for s in offspringPop3])
newGen = int(evaluations / self.report_interval)
if newGen > current_gen:
print("Hypervolume: " + str(newGen) + " - " + str(hVal1) +
" - " + str(hVal2) + " - " + str(hVal3))
combi = combi + pool_1 + pool_2 + offspringPop3
combi = self.r.replace(combi[:self.population_size * 2],
combi[self.population_size * 2:])
hval = h.compute([s.objectives for s in combi])
for j in range(current_gen, newGen):
generational_hv.append(hval)
current_gen = newGen
# return the final combined non-dominated set of maximum size = (populationSize * 2)
combiAll: List[FloatSolution] = []
combiAll = combiAll + pool_1 + pool_2 + pool_A
combiAll = self.r.replace(combiAll[:self.population_size * 2],
combiAll[self.population_size * 2:])
return combiAll