def update_solutions(self, solution, aggregation_function, sub_problem):
"""
Update solutions of the population and of the external archive ep
:param solution: {:class:`~moead_framework.solution.one_dimension_solution.OneDimensionSolution`} the candidate solution also called offspring
:param aggregation_function: {:class:`~moead_framework.aggregation.functions.AggregationFunction`} Aggregation function used to compare solution in a multi-objective context
:param sub_problem: {integer} index of the sub-problem currently visited
:return:
"""
for j in self.b[sub_problem]:
y_score = aggregation_function.run(
solution=solution,
number_of_objective=self.number_of_objective,
weights=self.weights,
sub_problem=j,
z=self.z)
pop_j_score = aggregation_function.run(
solution=self.population[j],
number_of_objective=self.number_of_objective,
weights=self.weights,
sub_problem=j,
z=self.z)
if aggregation_function.is_better(pop_j_score, y_score):
self.population[j] = solution
if not is_duplicated(
x=solution,
population=self.ep,
number_of_objective=self.number_of_objective):
self.ep.append(solution)
self.ep = get_non_dominated(self.ep)
def initial_population(self):
"""
Initialize the population of solution
:return: {List<:class:`~moead_framework.solution.one_dimension_solution.OneDimensionSolution`>}
"""
p = []
for i in range(self.number_of_weight):
x_i = self.problem.generate_random_solution()
p.append(x_i)
if not is_duplicated(x=x_i,
population=self.ep,
number_of_objective=self.number_of_objective):
self.ep.append(x_i)
self.ep = get_non_dominated(self.ep)
return p
def update_solutions(self, solution, aggregation_function, sub_problem):
"""
Update solutions of the population and of the external archive ep.
Integration of the parameter number_of_replacement (the maximal number of solutions replaced by each child solution
to preserve the diversity)
:param solution: {:class:`~moead_framework.solution.one_dimension_solution.OneDimensionSolution`} the candidate solution also called offspring
:param aggregation_function: {:class:`~moead_framework.aggregation.functions.AggregationFunction`} Aggregation function used to compare solution in a multi-objective context
:param sub_problem: {integer} index of the sub-problem currently visited
:return:
"""
c = 0
while (c < self.number_of_replacement) & (len(self.mating_pool) > 0):
# Step (1)
random_j = random.randint(0, len(self.mating_pool) - 1)
j = self.mating_pool[random_j]
j_score = aggregation_function.run(
solution=self.population[j],
number_of_objective=self.number_of_objective,
weights=self.weights,
sub_problem=j,
z=self.z)
y_score = aggregation_function.run(
solution=solution,
number_of_objective=self.number_of_objective,
weights=self.weights,
sub_problem=j,
z=self.z)
# Step (2)
if aggregation_function.is_better(j_score, y_score):
c += 1
self.population[j] = solution
if not is_duplicated(
x=solution,
population=self.ep,
number_of_objective=self.number_of_objective):
self.ep.append(solution)
self.ep = get_non_dominated(self.ep)
# Step (3)
self.mating_pool.remove(j)