def __HYPE(self) -> None:
reference_point = BinarySolution(self.__problem.number_of_variables, self.__problem.number_of_objectives, self.__problem.number_of_constraints)
reference_point.objectives = [1.0 for _ in range(self.__problem.number_of_objectives)]
self.__solver = HYPE(
problem=self.__problem,
reference_point=reference_point,
population_size=POPULATION_SIZE,
offspring_population_size=OFFSPRING_SIZE,
#mutation=BitFlipMutation(probability=1.0/self.__problem.number_of_variables),
mutation=BitFlipMutation(probability=0.035),
crossover=SPXCrossover(probability=1.0),
termination_criterion=StoppingByEvaluations(max_evaluations=MAX_EVALUATION)
)
def evaluate(self, solution: BinarySolution) -> BinarySolution:
budget = 0
objectives = self.number_of_objectives * [0.0]
for index, bits in enumerate(solution.variables[0]):
if bits:
budget += self.instance_.projects[index][0]
for obj in range(0, self.number_of_objectives):
objectives[obj] += self.instance_.projects[index][obj + 3]
solution.objectives = [-obj for obj in objectives]
solution.constraints = [self.budget - budget]
return solution
def evaluate(self, solution: BinarySolution) -> BinarySolution:
current_budget = Interval(0)
objectives = self.number_of_objectives * [Interval(0)]
for index, bits in enumerate(solution.variables[0]):
if bits:
current_budget += self.instance_.projects[index][0]
for obj in range(0, self.number_of_objectives):
objectives[obj] += self.instance_.projects[index][obj + 1]
poss = self.budget.poss_greater_than_or_eq(current_budget)
if poss < self.get_preference_model(0).chi:
solution.constraints = [self.budget - current_budget]
else:
solution.constraints = [0]
solution.budget = current_budget
solution.objectives = objectives
return solution
def evaluate(self, solution: BinarySolution) -> BinarySolution:
solution.objectives = [
func(self, solution) for func in self.objectives
]
return solution
