Source code for jmetal.problem.singleobjective.unconstrained

import math
import random

from jmetal.core.problem import BinaryProblem, FloatProblem
from jmetal.core.solution import BinarySolution, FloatSolution

"""
.. module:: unconstrained
   :platform: Unix, Windows
   :synopsis: Unconstrained test problems for single-objective optimization

.. moduleauthor:: Antonio J. Nebro <antonio@lcc.uma.es>, Antonio Benítez-Hidalgo <antonio.b@uma.es>
"""



[docs]
class OneMax(BinaryProblem):
    """ The implementation of the OneMax 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(OneMax, self).__init__()
        self.number_of_bits_per_variable = [number_of_bits]

        self.obj_directions = [self.MINIMIZE]
        self.obj_labels = ["Ones"]


[docs]
    def number_of_variables(self) -> int:
        return 1



[docs]
    def number_of_objectives(self) -> int:
        return 1



[docs]
    def number_of_constraints(self) -> int:
        return 0



[docs]
    def evaluate(self, solution: BinarySolution) -> BinarySolution:
        counter_of_ones = 0
        for bits in solution.variables[0]:
            if bits:
                counter_of_ones += 1

        solution.objectives[0] = -1.0 * counter_of_ones

        return solution



[docs]
    def create_solution(self) -> BinarySolution:
        new_solution = BinarySolution(number_of_variables=1, number_of_objectives=1)
        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 "OneMax"





[docs]
class Sphere(FloatProblem):
    def __init__(self, number_of_variables: int = 10):
        super(Sphere, self).__init__()

        self.obj_directions = [self.MINIMIZE]
        self.obj_labels = ["f(x)"]

        self.lower_bound = [-5.12 for _ in range(number_of_variables)]
        self.upper_bound = [5.12 for _ in range(number_of_variables)]

        FloatSolution.lower_bound = self.lower_bound
        FloatSolution.upper_bound = self.upper_bound


[docs]
    def number_of_objectives(self) -> int:
        return 1



[docs]
    def number_of_constraints(self) -> int:
        return 0



[docs]
    def evaluate(self, solution: FloatSolution) -> FloatSolution:
        total = 0.0
        for x in solution.variables:
            total += x * x

        solution.objectives[0] = total

        return solution



[docs]
    def name(self) -> str:
        return "Sphere"





[docs]
class Rastrigin(FloatProblem):
    def __init__(self, number_of_variables: int = 10):
        super(Rastrigin, self).__init__()

        self.obj_directions = [self.MINIMIZE]
        self.obj_labels = ["f(x)"]

        self.lower_bound = [-5.12 for _ in range(number_of_variables)]
        self.upper_bound = [5.12 for _ in range(number_of_variables)]

        FloatSolution.lower_bound = self.lower_bound
        FloatSolution.upper_bound = self.upper_bound


[docs]
    def number_of_objectives(self) -> int:
        return 1



[docs]
    def number_of_constraints(self) -> int:
        return 0



[docs]
    def evaluate(self, solution: FloatSolution) -> FloatSolution:
        a = 10.0
        result = a * len(solution.variables)
        x = solution.variables

        for i in range(len(solution.variables)):
            result += x[i] * x[i] - a * math.cos(2 * math.pi * x[i])

        solution.objectives[0] = result

        return solution



[docs]
    def name(self) -> str:
        return "Rastrigin"





[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.obj_directions = [self.MAXIMIZE]
        self.obj_labels = ["Sum"]


[docs]
    def number_of_variables(self) -> int:
        return 1


[docs]
    def number_of_objectives(self) -> int:
        return 1



[docs]
    def number_of_constraints(self) -> int:
        return 0



[docs]
    def evaluate(self, solution: BinarySolution) -> BinarySolution:
        total_sum = 0.0

        for index, bits in enumerate(solution.variables[0]):
            if bits:
                total_sum += self.W[index]

        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

        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"
Table Of Contents

Source code for jmetal.problem.singleobjective.unconstrained
