from math import cos, pi, sin, sqrt
from typing import List
from jmetal.core.problem import FloatProblem
from jmetal.core.solution import FloatSolution
[docs]
class LIRCMOP1(FloatProblem):
"""Class representing problem LIR-CMOP1, defined in:
* An Improved epsilon-constrained Method in MOEA/D for CMOPs with Large Infeasible Regions.
Fan, Z., Li, W., Cai, X. et al. Soft Comput (2019). https://doi.org/10.1007/s00500-019-03794-x
"""
def __init__(self, number_of_variables: int = 30):
super(LIRCMOP1, self).__init__()
self.obj_directions = [self.MINIMIZE, self.MINIMIZE]
self.obj_labels = ["f(x)", "f(y)"]
self.lower_bound = [0.0 for _ in range(number_of_variables)]
self.upper_bound = [1.0 for _ in range(number_of_variables)]
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def number_of_objectives(self) -> int:
return len(self.obj_directions)
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def number_of_constraints(self) -> int:
return 2
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def evaluate(self, solution: FloatSolution) -> FloatSolution:
x = solution.variables
solution.objectives[0] = x[0] + self.g1(x)
solution.objectives[1] = 1 - x[0] * x[0] + self.g2(x)
self.evaluate_constraints(solution)
return solution
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def evaluate_constraints(self, solution: FloatSolution) -> FloatSolution:
x = solution.variables
constraints = [0.0 for _ in range(len(solution.constraints))]
a = 0.51
b = 0.5
constraints[0] = (a - self.g1(x)) * (self.g1(x) - b)
constraints[1] = (a - self.g2(x)) * (self.g2(x) - b)
solution.constraints = constraints
return solution
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def g1(self, x: List[float]) -> float:
result = 0
for i in range(2, self.number_of_variables(), 2):
result += pow(x[i] - sin(0.5 * pi * x[0]), 2.0)
return result
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def g2(self, x: List[float]) -> float:
result = 0
for i in range(1, self.number_of_variables(), 2):
result += pow(x[i] - cos(0.5 * pi * x[0]), 2.0)
return result
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def name(self):
return "LIR-CMOP1"
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class LIRCMOP2(LIRCMOP1):
"""Class representing problem LIR-CMOP1, defined in:
* An Improved epsilon-constrained Method in MOEA/D for CMOPs with Large Infeasible Regions.
Fan, Z., Li, W., Cai, X. et al. Soft Comput (2019). https://doi.org/10.1007/s00500-019-03794-x
"""
def __init__(self, number_of_variables: int = 30):
super(LIRCMOP2, self).__init__(number_of_variables)
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def evaluate(self, solution: FloatSolution) -> FloatSolution:
x = solution.variables
solution.objectives[0] = x[0] + self.g1(x)
solution.objectives[1] = 1 - sqrt(x[0]) + self.g2(x)
self.evaluate_constraints(solution)
return solution
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def name(self):
return "LIR-CMOP2"
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class LIRCMOP3(LIRCMOP1):
"""Class representing problem LIR-CMOP3, defined in:
* An Improved epsilon-constrained Method in MOEA/D for CMOPs with Large Infeasible Regions.
Fan, Z., Li, W., Cai, X. et al. Soft Comput (2019). https://doi.org/10.1007/s00500-019-03794-x
"""
def __init__(self, number_of_variables: int = 30):
super(LIRCMOP3, self).__init__(number_of_variables)
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def number_of_constraints(self) -> int:
return 3
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def evaluate_constraints(self, solution: FloatSolution) -> FloatSolution:
x = solution.variables
constraints = [0.0 for _ in range(self.number_of_constraints())]
a = 0.51
b = 0.5
c = 20.0
constraints[0] = (a - self.g1(x)) * (self.g1(x) - b)
constraints[1] = (a - self.g2(x)) * (self.g2(x) - b)
constraints[2] = sin(c * pi * x[0]) - 0.5
solution.constraints = constraints
return solution
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def name(self):
return "LIR-CMOP3"
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class LIRCMOP4(LIRCMOP2):
"""Class representing problem LIR-CMOP4, defined in:
* An Improved epsilon-constrained Method in MOEA/D for CMOPs with Large Infeasible Regions.
Fan, Z., Li, W., Cai, X. et al. Soft Comput (2019). https://doi.org/10.1007/s00500-019-03794-x
"""
def __init__(self, number_of_variables: int = 30):
super(LIRCMOP4, self).__init__(number_of_variables)
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def number_of_constraints(self) -> int:
return 3
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def evaluate_constraints(self, solution: FloatSolution) -> FloatSolution:
x = solution.variables
constraints = [0.0 for _ in range(self.number_of_constraints())]
a = 0.51
b = 0.5
c = 20.0
constraints[0] = (a - self.g1(x)) * (self.g1(x) - b)
constraints[1] = (a - self.g2(x)) * (self.g2(x) - b)
constraints[2] = sin(c * pi * x[0]) - 0.5
solution.constraints = constraints
return solution
[docs]
def name(self):
return "LIR-CMOP4"
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class LIRCMOP5(FloatProblem):
"""Class representing problem LIR-CMOP5, defined in:
* An Improved epsilon-constrained Method in MOEA/D for CMOPs with Large Infeasible Regions.
Fan, Z., Li, W., Cai, X. et al. Soft Comput (2019). https://doi.org/10.1007/s00500-019-03794-x
"""
def __init__(self, number_of_variables: int = 30):
super(LIRCMOP5, self).__init__()
self.obj_directions = [self.MINIMIZE, self.MINIMIZE]
self.obj_labels = ["f(x)", "f(y)"]
self.lower_bound = [0.0 for _ in range(number_of_variables)]
self.upper_bound = [1.0 for _ in range(number_of_variables)]
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def number_of_objectives(self) -> int:
return len(self.obj_directions)
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def number_of_constraints(self) -> int:
return 2
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def evaluate(self, solution: FloatSolution) -> FloatSolution:
x = solution.variables
solution.objectives[0] = x[0] + 10 * self.g1(x) + 0.7057
solution.objectives[1] = 1 - sqrt(x[0]) + 10 * self.g2(x) + 7057
self.evaluate_constraints(solution)
return solution
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def evaluate_constraints(self, solution: FloatSolution) -> FloatSolution:
constraints = [0.0 for _ in range(self.number_of_constraints())]
r = 0.1
theta = -0.25 * pi
a_array = [2.0, 2.0]
b_array = [4.0, 8.0]
x_offset = [1.6, 2.5]
y_offset = [1.6, 2.5]
f1 = solution.objectives[0]
f2 = solution.objectives[1]
for i in range(len(x_offset)):
constraints[i] = (
pow(((f1 - x_offset[i]) * cos(theta) - (f2 - y_offset[i]) * sin(theta)) / a_array[i], 2)
+ pow(((f1 - x_offset[i]) * sin(theta) + (f2 - y_offset[i]) * cos(theta)) / b_array[i], 2)
- r
)
solution.constraints = constraints
return solution
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def g1(self, x: [float]) -> float:
result = 0
for i in range(2, self.number_of_variables(), 2):
result += pow(x[i] - sin(0.5 * i / len(x) * pi * x[0]), 2.0)
return result
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def g2(self, x: [float]) -> float:
result = 0
for i in range(1, self.number_of_variables(), 2):
result += pow(x[i] - cos(0.5 * i / len(x) * pi * x[0]), 2.0)
return result
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def name(self):
return "LIR-CMOP5"
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class LIRCMOP6(LIRCMOP5):
"""Class representing problem LIR-CMOP6, defined in:
* An Improved epsilon-constrained Method in MOEA/D for CMOPs with Large Infeasible Regions.
Fan, Z., Li, W., Cai, X. et al. Soft Comput (2019). https://doi.org/10.1007/s00500-019-03794-x
"""
def __init__(self, number_of_variables: int = 30):
super(LIRCMOP6, self).__init__(number_of_variables)
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def evaluate(self, solution: FloatSolution) -> FloatSolution:
x = solution.variables
solution.objectives[0] = x[0] + 10 * self.g1(x) + 0.7057
solution.objectives[1] = 1 - x[0] * x[0] + 10 * self.g2(x) + 7057
self.evaluate_constraints(solution)
return solution
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def evaluate_constraints(self, solution: FloatSolution) -> FloatSolution:
constraints = [0.0 for _ in range(self.number_of_constraints())]
r = 0.1
theta = -0.25 * pi
a_array = [2.0, 2.0]
b_array = [8.0, 8.0]
x_offset = [1.8, 2.8]
y_offset = [1.8, 2.8]
f1 = solution.objectives[0]
f2 = solution.objectives[1]
for i in range(len(x_offset)):
constraints[i] = (
pow(((f1 - x_offset[i]) * cos(theta) - (f2 - y_offset[i]) * sin(theta)) / a_array[i], 2)
+ pow(((f1 - x_offset[i]) * sin(theta) + (f2 - y_offset[i]) * cos(theta)) / b_array[i], 2)
- r
)
solution.constraints = constraints
return solution
[docs]
def name(self):
return "LIR-CMOP6"
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class LIRCMOP7(LIRCMOP5):
"""Class representing problem LIR-CMOP7, defined in:
* An Improved epsilon-constrained Method in MOEA/D for CMOPs with Large Infeasible Regions.
Fan, Z., Li, W., Cai, X. et al. Soft Comput (2019). https://doi.org/10.1007/s00500-019-03794-x
"""
def __init__(self, number_of_variables: int = 30):
super(LIRCMOP7, self).__init__(number_of_variables)
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def evaluate_constraints(self, solution: FloatSolution) -> FloatSolution:
constraints = [0.0 for _ in range(self.number_of_constraints())]
r = 0.1
theta = -0.25 * pi
a_array = [2.0, 2.5, 2.5]
b_array = [6.0, 12.0, 10.0]
x_offset = [1.2, 2.25, 3.5]
y_offset = [1.2, 2.25, 3.5]
f1 = solution.objectives[0]
f2 = solution.objectives[1]
for i in range(len(x_offset)):
constraints[i] = (
pow(((f1 - x_offset[i]) * cos(theta) - (f2 - y_offset[i]) * sin(theta)) / a_array[i], 2)
+ pow(((f1 - x_offset[i]) * sin(theta) + (f2 - y_offset[i]) * cos(theta)) / b_array[i], 2)
- r
)
solution.constraints = constraints
return solution
[docs]
def name(self):
return "LIR-CMOP7"
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class LIRCMOP8(LIRCMOP6):
"""Class representing problem LIR-CMOP8, defined in:
* An Improved epsilon-constrained Method in MOEA/D for CMOPs with Large Infeasible Regions.
Fan, Z., Li, W., Cai, X. et al. Soft Comput (2019). https://doi.org/10.1007/s00500-019-03794-x
"""
def __init__(self, number_of_variables: int = 30):
super(LIRCMOP8, self).__init__(number_of_variables)
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def evaluate_constraints(self, solution: FloatSolution) -> FloatSolution:
constraints = [0.0 for _ in range(self.number_of_constraints())]
r = 0.1
theta = -0.25 * pi
a_array = [2.0, 2.5, 2.5]
b_array = [6.0, 12.0, 10.0]
x_offset = [1.2, 2.25, 3.5]
y_offset = [1.2, 2.25, 3.5]
f1 = solution.objectives[0]
f2 = solution.objectives[1]
for i in range(len(x_offset)):
constraints[i] = (
pow(((f1 - x_offset[i]) * cos(theta) - (f2 - y_offset[i]) * sin(theta)) / a_array[i], 2)
+ pow(((f1 - x_offset[i]) * sin(theta) + (f2 - y_offset[i]) * cos(theta)) / b_array[i], 2)
- r
)
solution.constraints = constraints
return solution
[docs]
def name(self):
return "LIR-CMOP8"
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class LIRCMOP9(LIRCMOP8):
"""Class representing problem LIR-CMOP9, defined in:
* An Improved epsilon-constrained Method in MOEA/D for CMOPs with Large Infeasible Regions.
Fan, Z., Li, W., Cai, X. et al. Soft Comput (2019). https://doi.org/10.1007/s00500-019-03794-x
"""
def __init__(self, number_of_variables: int = 30):
super(LIRCMOP9, self).__init__(number_of_variables)
[docs]
def evaluate(self, solution: FloatSolution) -> FloatSolution:
x = solution.variables
solution.objectives[0] = 1.7057 * x[0] * (10 * self.g1(x) + 1)
solution.objectives[1] = 1.7957 * (1 - x[0] * x[0]) * (10 * self.g2(x) + 1)
self.evaluate_constraints(solution)
return solution
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def evaluate_constraints(self, solution: FloatSolution) -> FloatSolution:
x = solution.variables
constraints = [0.0 for _ in range(self.number_of_constraints())]
theta = -0.25 * pi
n = 4.0
f0 = solution.objectives[0]
f1 = solution.objectives[1]
constraints[0] = f0 * sin(theta) + f1 * cos(theta) - sin(n * pi * (f0 * cos(theta) - f1 * sin(theta))) - 2
x_offset = 1.40
y_offset = 1.40
a = 1.5
b = 6.0
r = 0.1
constraints[1] = (
pow(((f0 - x_offset) * cos(theta) - (f1 - y_offset) * sin(theta)) / a, 2)
+ pow(((f0 - x_offset) * sin(theta) + (f1 - y_offset) * cos(theta)) / b, 2)
- r
)
solution.constraints = constraints
return solution
[docs]
def name(self):
return "LIR-CMOP9"
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class LIRCMOP10(LIRCMOP8):
"""Class representing problem LIR-CMOP10, defined in:
* An Improved epsilon-constrained Method in MOEA/D for CMOPs with Large Infeasible Regions.
Fan, Z., Li, W., Cai, X. et al. Soft Comput (2019). https://doi.org/10.1007/s00500-019-03794-x
"""
def __init__(self, number_of_variables: int = 30):
super(LIRCMOP10, self).__init__(number_of_variables)
[docs]
def evaluate(self, solution: FloatSolution) -> FloatSolution:
x = solution.variables
solution.objectives[0] = 1.7057 * x[0] * (10 * self.g1(x) + 1)
solution.objectives[1] = 1.7957 * (1 - sqrt(x[0])) * (10 * self.g2(x) + 1)
self.evaluate_constraints(solution)
return solution
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def evaluate_constraints(self, solution: FloatSolution) -> FloatSolution:
constraints = [0.0 for _ in range(self.number_of_constraints())]
theta = -0.25 * pi
n = 4.0
f0 = solution.objectives[0]
f1 = solution.objectives[1]
constraints[0] = f0 * sin(theta) + f1 * cos(theta) - sin(n * pi * (f0 * cos(theta) - f1 * sin(theta))) - 1
x_offset = 1.1
y_offset = 1.2
a = 2.0
b = 4.0
r = 0.1
constraints[1] = (
pow(((f0 - x_offset) * cos(theta) - (f1 - y_offset) * sin(theta)) / a, 2)
+ pow(((f0 - x_offset) * sin(theta) + (f1 - y_offset) * cos(theta)) / b, 2)
- r
)
solution.constraints = constraints
return solution
[docs]
def name(self):
return "LIR-CMOP10"
[docs]
class LIRCMOP11(LIRCMOP10):
"""Class representing problem LIR-CMOP11, defined in:
* An Improved epsilon-constrained Method in MOEA/D for CMOPs with Large Infeasible Regions.
Fan, Z., Li, W., Cai, X. et al. Soft Comput (2019). https://doi.org/10.1007/s00500-019-03794-x
"""
def __init__(self, number_of_variables: int = 30):
super(LIRCMOP11, self).__init__(number_of_variables)
[docs]
def evaluate_constraints(self, solution: FloatSolution) -> FloatSolution:
constraints = [0.0 for _ in range(self.number_of_constraints())]
theta = -0.25 * pi
n = 4.0
f0 = solution.objectives[0]
f1 = solution.objectives[1]
constraints[0] = f0 * sin(theta) + f1 * cos(theta) - sin(n * pi * (f0 * cos(theta) - f1 * sin(theta))) - 2.1
x_offset = 1.2
y_offset = 1.2
a = 1.5
b = 5.0
r = 0.1
constraints[1] = (
pow(((f0 - x_offset) * cos(theta) - (f1 - y_offset) * sin(theta)) / a, 2)
+ pow(((f0 - x_offset) * sin(theta) + (f1 - y_offset) * cos(theta)) / b, 2)
- r
)
solution.constraints = constraints
return solution
[docs]
def name(self):
return "LIR-CMOP11"
[docs]
class LIRCMOP12(LIRCMOP9):
"""Class representing problem LIR-CMOP9, defined in:
* An Improved epsilon-constrained Method in MOEA/D for CMOPs with Large Infeasible Regions.
Fan, Z., Li, W., Cai, X. et al. Soft Comput (2019). https://doi.org/10.1007/s00500-019-03794-x
"""
def __init__(self, number_of_variables: int = 30):
super(LIRCMOP12, self).__init__(number_of_variables)
[docs]
def evaluate_constraints(self, solution: FloatSolution) -> FloatSolution:
constraints = [0.0 for _ in range(self.number_of_constraints())]
theta = -0.25 * pi
n = 4.0
f0 = solution.objectives[0]
f1 = solution.objectives[1]
constraints[0] = f0 * sin(theta) + f1 * cos(theta) - sin(n * pi * (f0 * cos(theta) - f1 * sin(theta))) - 2.5
x_offset = 1.6
y_offset = 1.6
a = 1.5
b = 6.0
r = 0.1
constraints[1] = (
pow(((f0 - x_offset) * cos(theta) - (f1 - y_offset) * sin(theta)) / a, 2)
+ pow(((f0 - x_offset) * sin(theta) + (f1 - y_offset) * cos(theta)) / b, 2)
- r
)
solution.constraints = constraints
return solution
[docs]
def name(self):
return "LIR-CMOP12"
[docs]
class LIRCMOP13(FloatProblem):
"""Class representing problem LIR-CMOP13, defined in:
* An Improved epsilon-constrained Method in MOEA/D for CMOPs with Large Infeasible Regions.
Fan, Z., Li, W., Cai, X. et al. Soft Comput (2019). https://doi.org/10.1007/s00500-019-03794-x
"""
def __init__(self, number_of_variables: int = 30):
super(LIRCMOP13, self).__init__()
self.obj_directions = [self.MINIMIZE, self.MINIMIZE]
self.obj_labels = ["f(x)", "f(y)"]
self.lower_bound = [0.0 for _ in range(number_of_variables)]
self.upper_bound = [1.0 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 2
[docs]
def evaluate(self, solution: FloatSolution) -> FloatSolution:
x = solution.variables
solution.objectives[0] = (1.7057 + self.g1(x)) * cos(0.5 * pi * x[0]) * cos(0.5 * pi + x[1])
solution.objectives[1] = (1.7057 + self.g1(x)) * cos(0.5 * pi * x[0]) * sin(0.5 * pi + x[1])
solution.objectives[2] = (1.7057 + self.g1(x)) * sin(0.5 * pi + x[0])
self.evaluate_constraints(solution)
return solution
[docs]
def evaluate_constraints(self, solution: FloatSolution) -> FloatSolution:
constraints = [0.0 for _ in range(self.number_of_constraints())]
f = sum([pow(solution.objectives[i], 2) for i in range(len(solution.objectives))])
constraints[0] = (f - 9) * (f - 4)
constraints[1] = (f - 1.9 * 1.9) * (f - 1.8 * 1.8)
solution.constraints = constraints
return solution
[docs]
def g1(self, x: [float]) -> float:
result = 0
for i in range(2, self.number_of_variables(), 2):
result += 10 * pow(x[i] - 0.5, 2.0)
return result
[docs]
def name(self):
return "LIR-CMOP13"
[docs]
class LIRCMOP14(LIRCMOP13):
"""Class representing problem LIR-CMOP14, defined in:
* An Improved epsilon-constrained Method in MOEA/D for CMOPs with Large Infeasible Regions.
Fan, Z., Li, W., Cai, X. et al. Soft Comput (2019). https://doi.org/10.1007/s00500-019-03794-x
"""
def __init__(self, number_of_variables: int = 30):
super(LIRCMOP14, self).__init__(number_of_variables)
[docs]
def number_of_objectives(self) -> int:
return len(self.obj_directions)
[docs]
def number_of_constraints(self) -> int:
return 3
[docs]
def evaluate_constraints(self, solution: FloatSolution) -> FloatSolution:
constraints = [0.0 for _ in range(self.number_of_constraints())]
f = sum([pow(solution.objectives[i], 2) for i in range(len(solution.objectives))])
constraints[0] = (f - 9) * (f - 4)
constraints[1] = (f - 1.9 * 1.9) * (f - 1.8 * 1.8)
constraints[2] = (f - 1.75 * 1.75) * (f - 1.6 * 1.6)
solution.constraints = constraints
return solution
[docs]
def name(self):
return "LIR-CMOP14"
