def optimal_solution(self, k, l, pos_params):
"""Create one Pareto-optimal solution with given position parameters."""
# the result vector
result = []
result.extend(pos_params)
# set the distance parameters
for i in range(k, k + l):
result.append(0.35)
# scale to the correct domains
for i in range(k + l):
result[i] *= 2.0 * (i + 1)
ind = Individual()
ind.phenome = result
return ind
def get_optimal_solutions(self, max_number=100):
"""Return Pareto-optimal solutions.
.. note:: The returned solutions do not yet contain the objective
values.
Parameters
----------
max_number : int, optional
As the number of Pareto-optimal solutions is infinite, the
returned set has to be restricted to a finite sample.
Returns
-------
solutions : list of Individual
The Pareto-optimal solutions
"""
assert max_number > 1
solutions = []
for i in range(max_number):
phenome = [0.0] * self.num_variables
phenome[0] = float(i) / float(max_number - 1)
solutions.append(Individual(phenome))
return solutions
def get_optimal_solutions(self, max_number=None):
"""Return globally optimal solutions.
Parameters
----------
max_number : int, optional
Potentially restrict the number of optima.
Returns
-------
optima : list of Individual
"""
# test peaks
optima = []
max_height = -1.0
opt_phenomes = []
for peak in self.peaks:
if peak.height > max_height:
opt_phenomes = [peak]
max_height = peak.height
elif peak.height == max_height:
opt_phenomes.append(peak)
for phenome in opt_phenomes:
optima.append(Individual(phenome=list(phenome)))
if max_number is not None:
optima = optima[:max_number]
return optima
def get_optimal_solutions(self, max_number=None):
"""Return Pareto-optimal solutions.
.. note:: The returned solutions do not yet contain the objective
values.
Parameters
----------
max_number : int, optional
Optionally restrict the number of solutions.
Returns
-------
solutions : list of Individual
The Pareto-optimal solutions
"""
assert max_number is None or max_number > 0
individuals = []
for i in range(self.num_variables + 1):
opt = Individual([1] * i + [0] * (self.num_variables - i))
individuals.append(opt)
if max_number is not None:
individuals = individuals[:max_number]
return individuals
def optimal_solution(self, k, l, pos_params):
"""Create one Pareto-optimal solution with given position parameters."""
result = []
result.extend(pos_params)
# calculate the distance parameters
for i in range(k, k + l):
w = [1.0] * len(result)
u = r_sum(result, w)
tmp1 = abs(math.floor(0.5 - u) + 0.98 / 49.98)
tmp2 = 0.02 + 49.98 * (0.98 / 49.98 - (1.0 - 2.0 * u) * tmp1)
result.append(pow(0.35, pow(tmp2, -1.0)))
# scale to the correct domains
for i in range(k + l):
result[i] *= 2.0 * (i + 1)
ind = Individual()
ind.phenome = result
return ind
def optimal_solution(self, k, l, pos_params):
"""Create one Pareto-optimal solution with given position parameters."""
result = []
result.extend(pos_params)
result.extend([0.0] * l)
# calculate the distance parameters
result[k + l - 1] = 0.35
for i in range(k + l - 2, k - 1, -1):
result_sub = []
for j in range(i + 1, k + l):
result_sub.append(result[j])
w = [1.0] * len(result_sub)
tmp1 = r_sum(result_sub, w)
result[i] = pow(0.35, pow(0.02 + 1.96 * tmp1, -1.0))
# scale to the correct domains
for i in range(k + l):
result[i] *= 2.0 * (i + 1)
ind = Individual()
ind.phenome = result
return ind
def get_optimal_solutions(self, max_number=None):
"""Return the optimal solution.
.. note:: The returned solution does not yet contain the objective
values.
Returns
-------
solutions : list of Individual
"""
assert max_number is None or max_number > 0
opt = Individual([1] * self.num_variables)
return [opt]
def get_locally_optimal_solutions(self, max_number=None):
"""Return locally optimal solutions (includes global ones).
Parameters
----------
max_number : int, optional
Potentially restrict the number of optima.
Returns
-------
optima : list of Individual
"""
get_active_peak = self.get_active_peak
# test peaks
local_optima = []
peaks = np.vstack(self.peaks)
for i, peak in enumerate(peaks):
if get_active_peak(peak) is self.peaks[i]:
local_optima.append(Individual(phenome=list(peak)))
if max_number is not None:
local_optima = local_optima[:max_number]
return local_optima
def get_optimal_solutions(self, max_number=None):
return [Individual(self.offsets[0][:self.num_variables])]