import random
from math import exp, pow, sin, sqrt
from jmetal.core.problem import BinaryProblem, FloatProblem, Problem
from jmetal.core.solution import (
BinarySolution,
CompositeSolution,
FloatSolution,
IntegerSolution,
)
"""
.. module:: constrained
:platform: Unix, Windows
:synopsis: Unconstrained test problems for multi-objective optimization
.. moduleauthor:: Antonio J. Nebro <antonio@lcc.uma.es>
"""
[docs]
class Kursawe(FloatProblem):
"""Class representing problem Kursawe."""
def __init__(self, number_of_variables: int = 3):
super(Kursawe, self).__init__()
self.obj_directions = [self.MINIMIZE, self.MINIMIZE]
self.obj_labels = ["f(x)", "f(y)"]
self.lower_bound = [-5.0 for _ in range(number_of_variables)]
self.upper_bound = [5.0 for _ in range(number_of_variables)]
[docs]
def number_of_objectives(self) -> int:
return len(self.obj_directions)
[docs]
def number_of_constraints(self) -> int:
return 0
[docs]
def evaluate(self, solution: FloatSolution) -> FloatSolution:
fx = [0.0 for _ in range(self.number_of_objectives())]
for i in range(self.number_of_variables() - 1):
xi = solution.variables[i] * solution.variables[i]
xj = solution.variables[i + 1] * solution.variables[i + 1]
aux = -0.2 * sqrt(xi + xj)
fx[0] += -10 * exp(aux)
for i in range(self.number_of_variables()):
fx[1] += pow(abs(solution.variables[i]), 0.8) + 5.0 * sin(pow(solution.variables[i], 3.0))
solution.objectives[0] = fx[0]
solution.objectives[1] = fx[1]
return solution
[docs]
def name(self):
return "Kursawe"
[docs]
class Fonseca(FloatProblem):
def __init__(self):
super(Fonseca, self).__init__()
self.obj_directions = [self.MINIMIZE, self.MINIMIZE]
self.obj_labels = ["f(x)", "f(y)"]
number_of_variables = 3
self.lower_bound = number_of_variables * [-4]
self.upper_bound = number_of_variables * [4]
[docs]
def number_of_objectives(self) -> int:
return len(self.obj_directions)
[docs]
def number_of_constraints(self) -> int:
return 0
[docs]
def evaluate(self, solution: FloatSolution) -> FloatSolution:
n = self.number_of_variables()
solution.objectives[0] = 1 - exp(-sum([(x - 1.0 / n**0.5) ** 2 for x in solution.variables]))
solution.objectives[1] = 1 - exp(-sum([(x + 1.0 / n**0.5) ** 2 for x in solution.variables]))
return solution
[docs]
def name(self):
return "Fonseca"
[docs]
class Schaffer(FloatProblem):
def __init__(self):
super(Schaffer, self).__init__()
self.obj_directions = [self.MINIMIZE, self.MINIMIZE]
self.obj_labels = ["f(x)", "f(y)"]
self.lower_bound = [-1000]
self.upper_bound = [1000]
[docs]
def number_of_objectives(self) -> int:
return len(self.obj_directions)
[docs]
def number_of_constraints(self) -> int:
return 0
[docs]
def evaluate(self, solution: FloatSolution) -> FloatSolution:
value = solution.variables[0]
solution.objectives[0] = value**2
solution.objectives[1] = (value - 2) ** 2
return solution
[docs]
def name(self):
return "Schaffer"
[docs]
class Viennet2(FloatProblem):
def __init__(self):
super(Viennet2, self).__init__()
self.obj_directions = [self.MINIMIZE, self.MINIMIZE, self.MINIMIZE]
self.obj_labels = ["f(x)", "f(y)", "f(z)"]
number_of_variables = 2
self.lower_bound = number_of_variables * [-4]
self.upper_bound = number_of_variables * [4]
[docs]
def number_of_objectives(self) -> int:
return len(self.obj_directions)
[docs]
def number_of_constraints(self) -> int:
return 0
[docs]
def evaluate(self, solution: FloatSolution) -> FloatSolution:
x0 = solution.variables[0]
x1 = solution.variables[1]
f1 = (x0 - 2) * (x0 - 2) / 2.0 + (x1 + 1) * (x1 + 1) / 13.0 + 3.0
f2 = (x0 + x1 - 3.0) * (x0 + x1 - 3.0) / 36.0 + (-x0 + x1 + 2.0) * (-x0 + x1 + 2.0) / 8.0 - 17.0
f3 = (x0 + 2 * x1 - 1) * (x0 + 2 * x1 - 1) / 175.0 + (2 * x1 - x0) * (2 * x1 - x0) / 17.0 - 13.0
solution.objectives[0] = f1
solution.objectives[1] = f2
solution.objectives[2] = f3
return solution
[docs]
def name(self):
return "Viennet2"
[docs]
class SubsetSum(BinaryProblem):
def __init__(self, C: int, W: list):
"""The goal is to find a subset S of W whose elements sum is closest to (without exceeding) C.
:param C: Large integer.
:param W: Set of non-negative integers."""
super(SubsetSum, self).__init__()
self.C = C
self.W = W
self.number_of_bits = len(self.W)
self.number_of_objectives = 2
self.number_of_variables = 1
self.number_of_constraints = 0
self.obj_directions = [self.MAXIMIZE, self.MINIMIZE]
self.obj_labels = ["Sum", "No. of Objects"]
[docs]
def evaluate(self, solution: BinarySolution) -> BinarySolution:
total_sum = 0.0
number_of_objects = 0
for index, bits in enumerate(solution.variables[0]):
if bits:
total_sum += self.W[index]
number_of_objects += 1
if total_sum > self.C:
total_sum = self.C - total_sum * 0.1
if total_sum < 0.0:
total_sum = 0.0
solution.objectives[0] = -1.0 * total_sum
solution.objectives[1] = number_of_objects
return solution
[docs]
def create_solution(self) -> BinarySolution:
new_solution = BinarySolution(
number_of_variables=self.number_of_variables, number_of_objectives=self.number_of_objectives
)
new_solution.variables[0] = [True if random.randint(0, 1) == 0 else False for _ in range(self.number_of_bits)]
return new_solution
[docs]
def name(self) -> str:
return "Subset Sum"
[docs]
class OneZeroMax(BinaryProblem):
""" The implementation of the OneZeroMax problems defines a single binary variable. This variable
will contain the bit string representing the solutions.
"""
def __init__(self, number_of_bits: int = 256):
super(OneZeroMax, self).__init__()
self.number_of_bits_per_variable = [number_of_bits]
self.obj_directions = [self.MINIMIZE, self.MINIMIZE]
self.obj_labels = ["Zeroes", "Ones"]
[docs]
def number_of_variables(self) -> int:
return 1
[docs]
def number_of_objectives(self) -> int:
return 2
[docs]
def number_of_constraints(self) -> int:
return 0
[docs]
def evaluate(self, solution: BinarySolution) -> BinarySolution:
counter_of_ones = 0
counter_of_zeroes = 0
for bits in solution.variables[0]:
if bits:
counter_of_ones += 1
else:
counter_of_zeroes += 1
solution.objectives[0] = -1.0 * counter_of_ones
solution.objectives[1] = -1.0 * counter_of_zeroes
return solution
[docs]
def create_solution(self) -> BinarySolution:
new_solution = BinarySolution(
number_of_variables=self.number_of_variables(), number_of_objectives=self.number_of_objectives()
)
new_solution.variables[0] = [True if random.randint(0, 1) == 0 else False for _ in range(self.number_of_bits_per_variable[0])]
return new_solution
[docs]
def name(self) -> str:
return "OneZeroMax"
[docs]
class MixedIntegerFloatProblem(Problem):
def __init__(
self,
number_of_integer_variables=10,
number_of_float_variables=10,
n=100,
m=-100,
lower_bound=-1000,
upper_bound=1000,
):
super(MixedIntegerFloatProblem, self).__init__()
self.number_of_objectives = 2
self.number_of_variables = 2
self.number_of_constraints = 0
self.n = n
self.m = m
self.float_lower_bound = [lower_bound for _ in range(number_of_float_variables)]
self.float_upper_bound = [upper_bound for _ in range(number_of_float_variables)]
self.int_lower_bound = [lower_bound for _ in range(number_of_integer_variables)]
self.int_upper_bound = [upper_bound for _ in range(number_of_integer_variables)]
self.obj_directions = [self.MINIMIZE]
self.obj_labels = ["Ones"]
[docs]
def evaluate(self, solution: CompositeSolution) -> CompositeSolution:
distance_to_n = sum([abs(self.n - value) for value in solution.variables[0].variables])
distance_to_m = sum([abs(self.m - value) for value in solution.variables[0].variables])
distance_to_n += sum([abs(self.n - value) for value in solution.variables[1].variables])
distance_to_m += sum([abs(self.m - value) for value in solution.variables[1].variables])
solution.objectives[0] = distance_to_n
solution.objectives[1] = distance_to_m
return solution
[docs]
def create_solution(self) -> CompositeSolution:
integer_solution = IntegerSolution(
self.int_lower_bound, self.int_upper_bound, self.number_of_objectives, self.number_of_constraints
)
float_solution = FloatSolution(
self.float_lower_bound, self.float_upper_bound, self.number_of_objectives, self.number_of_constraints
)
float_solution.variables = [
random.uniform(self.float_lower_bound[i] * 1.0, self.float_upper_bound[i] * 1.0)
for i in range(len(self.float_lower_bound))
]
integer_solution.variables = [
random.uniform(self.int_lower_bound[i], self.int_upper_bound[i])
for i in range(len(self.int_lower_bound))
]
return CompositeSolution([integer_solution, float_solution])
[docs]
def name(self) -> str:
return "Mixed Integer Float Problem"
