def solve(self):
# the result object to be finally returned
res = Result()
# set the timer in the beginning of the call
res.start_time = time.time()
# call the algorithm to solve the problem
self._solve(self.problem)
# store the time when the algorithm as finished
res.end_time = time.time()
res.exec_time = res.end_time - res.start_time
# store the resulting population
res.pop = self.pop
# get the optimal solution found
opt = self.opt
# if optimum is not set
if len(opt) == 0:
opt = None
# if no feasible solution has been found
elif not np.any(opt.get("feasible")):
if self.return_least_infeasible:
opt = filter_optimum(opt, least_infeasible=True)
else:
opt = None
# set the optimum to the result object
res.opt = opt
# if optimum is set to none to not report anything
if opt is None:
X, F, CV, G = None, None, None, None
# otherwise get the values from the population
else:
X, F, CV, G = self.opt.get("X", "F", "CV", "G")
# if single-objective problem and only one solution was found - create a 1d array
if self.problem.n_obj == 1 and len(X) == 1:
X, F, CV, G = X[0], F[0], CV[0], G[0]
# set all the individual values
res.X, res.F, res.CV, res.G = X, F, CV, G
# create the result object
res.problem, res.pf = self.problem, self.pf
res.history = self.history
return res
def solve(self):
# the result object to be finally returned
res = Result()
# set the timer in the beginning of the call
res.start_time = time.time()
# call the algorithm to solve the problem
self._solve()
# create the result object based on the current iteration
res = self.result()
return res