#!/usr/bin/env python
# Created by "Thieu" at 22:19, 01/07/2022 ----------%
# Email: nguyenthieu2102@gmail.com %
# Github: https://github.com/thieu1995 %
# --------------------------------------------------%
import numpy as np
from opfunu.cec_based.cec import CecBenchmark
from opfunu.utils import operator
[docs]class F12008(CecBenchmark):
"""
.. [1] Tang, K., Yáo, X., Suganthan, P. N., MacNish, C., Chen, Y. P., Chen, C. M., & Yang, Z. (2007). Benchmark functions
for the CEC’2008 special session and competition on large scale global optimization.
Nature inspired computation and applications laboratory, USTC, China, 24, 1-18.
"""
name = "F1: Shifted Sphere Function"
latex_formula = r'F_1(x) = \sum_{i=1}^D z_i^2 + bias, z=x-o,\\ x=[x_1, ..., x_D]; o=[o_1, ..., o_D]: \text{the shifted global optimum}'
latex_formula_dimension = r'2 <= D <= 100'
latex_formula_bounds = r'x_i \in [-100.0, 100.0], \forall i \in [1, D]'
latex_formula_global_optimum = r'\text{Global optimum: } x^* = o, F_1(x^*) = bias = -450.0'
continuous = True
linear = False
convex = True
unimodal = True
separable = True
differentiable = True
scalable = True
randomized_term = False
parametric = True
shifted = True
rotated = False
modality = False # Number of ambiguous peaks, unknown # peaks
# n_basins = 1
# n_valleys = 1
def __init__(self, ndim=None, bounds=None, f_shift="sphere_shift_func_data", f_bias=-450.):
super().__init__()
self.dim_changeable = True
self.dim_default = 500
self.dim_max = 1000
self.check_ndim_and_bounds(ndim, self.dim_max, bounds, np.array([[-100., 100.] for _ in range(self.dim_default)]))
self.make_support_data_path("data_2008")
self.f_shift = self.check_shift_data(f_shift)[:self.ndim]
self.f_bias = f_bias
self.f_global = f_bias
self.x_global = self.f_shift
self.paras = {"f_shift": self.f_shift, "f_bias": self.f_bias}
[docs] def evaluate(self, x, *args):
self.n_fe += 1
self.check_solution(x, self.dim_max, self.dim_supported)
return operator.sphere_func(x - self.f_shift) + self.f_bias
[docs]class F22008(CecBenchmark):
"""
.. [1] Tang, K., Yáo, X., Suganthan, P. N., MacNish, C., Chen, Y. P., Chen, C. M., & Yang, Z. (2007). Benchmark functions
for the CEC’2008 special session and competition on large scale global optimization.
Nature inspired computation and applications laboratory, USTC, China, 24, 1-18.
"""
name = "F2: Schwefel’s Problem 2.21"
latex_formula = r'F_1(x) = \sum_{i=1}^D z_i^2 + bias, z=x-o,\\ x=[x_1, ..., x_D]; o=[o_1, ..., o_D]: \text{the shifted global optimum}'
latex_formula_dimension = r'2 <= D <= 100'
latex_formula_bounds = r'x_i \in [-100.0, 100.0], \forall i \in [1, D]'
latex_formula_global_optimum = r'\text{Global optimum: } x^* = o, F_1(x^*) = bias = -450.0'
continuous = False
linear = True
convex = True
unimodal = True
separable = False
differentiable = False
scalable = True
randomized_term = False
parametric = True
shifted = True
rotated = False
modality = False # Number of ambiguous peaks, unknown # peaks
# n_basins = 1
# n_valleys = 1
def __init__(self, ndim=None, bounds=None, f_shift="schwefel_shift_func_data", f_bias=-450.):
super().__init__()
self.dim_changeable = True
self.dim_default = 500
self.dim_max = 1000
self.check_ndim_and_bounds(ndim, self.dim_max, bounds, np.array([[-100., 100.] for _ in range(self.dim_default)]))
self.make_support_data_path("data_2008")
self.f_shift = self.check_shift_data(f_shift)[:self.ndim]
self.f_bias = f_bias
self.f_global = f_bias
self.x_global = self.f_shift
self.paras = {"f_shift": self.f_shift, "f_bias": self.f_bias}
[docs] def evaluate(self, x, *args):
self.n_fe += 1
self.check_solution(x, self.dim_max, self.dim_supported)
return np.max(np.abs(x - self.f_shift)) + self.f_bias
[docs]class F32008(CecBenchmark):
"""
.. [1] Tang, K., Yáo, X., Suganthan, P. N., MacNish, C., Chen, Y. P., Chen, C. M., & Yang, Z. (2007). Benchmark functions
for the CEC’2008 special session and competition on large scale global optimization.
Nature inspired computation and applications laboratory, USTC, China, 24, 1-18.
"""
name = "F3: Shifted Rosenbrock’s Function"
latex_formula = r'F_1(x) = \sum_{i=1}^D z_i^2 + bias, z=x-o,\\ x=[x_1, ..., x_D]; o=[o_1, ..., o_D]: \text{the shifted global optimum}'
latex_formula_dimension = r'2 <= D <= 100'
latex_formula_bounds = r'x_i \in [-100.0, 100.0], \forall i \in [1, D]'
latex_formula_global_optimum = r'\text{Global optimum: } x^* = o, F_1(x^*) = bias = -450.0'
continuous = True
linear = False
convex = True
unimodal = False
separable = False
differentiable = True
scalable = True
randomized_term = False
parametric = True
shifted = True
rotated = False
modality = False # Number of ambiguous peaks, unknown # peaks
# n_basins = 1
# n_valleys = 1
def __init__(self, ndim=None, bounds=None, f_shift="rosenbrock_shift_func_data", f_bias=-390.):
super().__init__()
self.dim_changeable = True
self.dim_default = 500
self.dim_max = 1000
self.check_ndim_and_bounds(ndim, self.dim_max, bounds, np.array([[-100., 100.] for _ in range(self.dim_default)]))
self.make_support_data_path("data_2008")
self.f_shift = self.check_shift_data(f_shift)[:self.ndim]
self.f_bias = f_bias
self.f_global = f_bias
self.x_global = self.f_shift
self.paras = {"f_shift": self.f_shift, "f_bias": self.f_bias}
[docs] def evaluate(self, x, *args):
self.n_fe += 1
self.check_solution(x, self.dim_max, self.dim_supported)
return operator.rosenbrock_func(x - self.f_shift, shift=1.0) + self.f_bias
[docs]class F42008(CecBenchmark):
"""
.. [1] Tang, K., Yáo, X., Suganthan, P. N., MacNish, C., Chen, Y. P., Chen, C. M., & Yang, Z. (2007). Benchmark functions
for the CEC’2008 special session and competition on large scale global optimization.
Nature inspired computation and applications laboratory, USTC, China, 24, 1-18.
"""
name = "F4: Shifted Rastrigin’s Function"
latex_formula = r'F_1(x) = \sum_{i=1}^D z_i^2 + bias, z=x-o,\\ x=[x_1, ..., x_D]; o=[o_1, ..., o_D]: \text{the shifted global optimum}'
latex_formula_dimension = r'2 <= D <= 100'
latex_formula_bounds = r'x_i \in [-100.0, 100.0], \forall i \in [1, D]'
latex_formula_global_optimum = r'\text{Global optimum: } x^* = o, F_1(x^*) = bias = -450.0'
continuous = True
linear = False
convex = True
unimodal = False
separable = True
differentiable = True
scalable = True
randomized_term = False
parametric = True
shifted = True
rotated = False
modality = True # Number of ambiguous peaks, unknown # peaks
# n_basins = 1
# n_valleys = 1
def __init__(self, ndim=None, bounds=None, f_shift="rastrigin_shift_func_data", f_bias=-330.):
super().__init__()
self.dim_changeable = True
self.dim_default = 500
self.dim_max = 1000
self.check_ndim_and_bounds(ndim, self.dim_max, bounds, np.array([[-5., 5.] for _ in range(self.dim_default)]))
self.make_support_data_path("data_2008")
self.f_shift = self.check_shift_data(f_shift)[:self.ndim]
self.f_bias = f_bias
self.f_global = f_bias
self.x_global = self.f_shift
self.paras = {"f_shift": self.f_shift, "f_bias": self.f_bias}
[docs] def evaluate(self, x, *args):
self.n_fe += 1
self.check_solution(x, self.dim_max, self.dim_supported)
z = x - self.f_shift
return operator.rastrigin_func(z) + self.f_bias
[docs]class F52008(CecBenchmark):
"""
.. [1] Tang, K., Yáo, X., Suganthan, P. N., MacNish, C., Chen, Y. P., Chen, C. M., & Yang, Z. (2007). Benchmark functions
for the CEC’2008 special session and competition on large scale global optimization.
Nature inspired computation and applications laboratory, USTC, China, 24, 1-18.
"""
name = "F5: Shifted Griewank’s Function"
latex_formula = r'F_1(x) = \sum_{i=1}^D z_i^2 + bias, z=x-o,\\ x=[x_1, ..., x_D]; o=[o_1, ..., o_D]: \text{the shifted global optimum}'
latex_formula_dimension = r'2 <= D <= 100'
latex_formula_bounds = r'x_i \in [-100.0, 100.0], \forall i \in [1, D]'
latex_formula_global_optimum = r'\text{Global optimum: } x^* = o, F_1(x^*) = bias = -450.0'
continuous = True
linear = False
convex = True
unimodal = False
separable = False
differentiable = True
scalable = True
randomized_term = False
parametric = True
shifted = True
rotated = False
modality = False # Number of ambiguous peaks, unknown # peaks
# n_basins = 1
# n_valleys = 1
def __init__(self, ndim=None, bounds=None, f_shift="griewank_shift_func_data", f_bias=-180.):
super().__init__()
self.dim_changeable = True
self.dim_default = 500
self.dim_max = 1000
self.check_ndim_and_bounds(ndim, self.dim_max, bounds, np.array([[-600., 600.] for _ in range(self.dim_default)]))
self.make_support_data_path("data_2008")
self.f_shift = self.check_shift_data(f_shift)[:self.ndim]
self.f_bias = f_bias
self.f_global = f_bias
self.x_global = self.f_shift
self.paras = {"f_shift": self.f_shift, "f_bias": self.f_bias}
[docs] def evaluate(self, x, *args):
self.n_fe += 1
self.check_solution(x, self.dim_max, self.dim_supported)
z = x - self.f_shift
return operator.griewank_func(z) + self.f_bias
[docs]class F62008(CecBenchmark):
"""
.. [1] Tang, K., Yáo, X., Suganthan, P. N., MacNish, C., Chen, Y. P., Chen, C. M., & Yang, Z. (2007). Benchmark functions
for the CEC’2008 special session and competition on large scale global optimization.
Nature inspired computation and applications laboratory, USTC, China, 24, 1-18.
"""
name = "F6: Shifted Ackley’s Function"
latex_formula = r'F_1(x) = \sum_{i=1}^D z_i^2 + bias, z=x-o,\\ x=[x_1, ..., x_D]; o=[o_1, ..., o_D]: \text{the shifted global optimum}'
latex_formula_dimension = r'2 <= D <= 100'
latex_formula_bounds = r'x_i \in [-100.0, 100.0], \forall i \in [1, D]'
latex_formula_global_optimum = r'\text{Global optimum: } x^* = o, F_1(x^*) = bias = -450.0'
continuous = True
linear = False
convex = True
unimodal = False
separable = True
differentiable = True
scalable = True
randomized_term = False
parametric = True
shifted = True
rotated = False
modality = False # Number of ambiguous peaks, unknown # peaks
# n_basins = 1
# n_valleys = 1
def __init__(self, ndim=None, bounds=None, f_shift="ackley_shift_func_data", f_bias=-140.):
super().__init__()
self.dim_changeable = True
self.dim_default = 500
self.dim_max = 1000
self.check_ndim_and_bounds(ndim, self.dim_max, bounds, np.array([[-32., 32.] for _ in range(self.dim_default)]))
self.make_support_data_path("data_2008")
self.f_shift = self.check_shift_data(f_shift)[:self.ndim]
self.f_bias = f_bias
self.f_global = f_bias
self.x_global = self.f_shift
self.paras = {"f_shift": self.f_shift, "f_bias": self.f_bias}
[docs] def evaluate(self, x, *args):
self.n_fe += 1
self.check_solution(x, self.dim_max, self.dim_supported)
z = x - self.f_shift
return operator.ackley_func(z) + self.f_bias
[docs]class F72008(CecBenchmark):
"""
.. [1] Tang, K., Yáo, X., Suganthan, P. N., MacNish, C., Chen, Y. P., Chen, C. M., & Yang, Z. (2007). Benchmark functions
for the CEC’2008 special session and competition on large scale global optimization.
Nature inspired computation and applications laboratory, USTC, China, 24, 1-18.
"""
name = "F7: FastFractal “DoubleDip” Function"
latex_formula = r'F_1(x) = \sum_{i=1}^D z_i^2 + bias, z=x-o,\\ x=[x_1, ..., x_D]; o=[o_1, ..., o_D]: \text{the shifted global optimum}'
latex_formula_dimension = r'2 <= D <= 100'
latex_formula_bounds = r'x_i \in [-100.0, 100.0], \forall i \in [1, D]'
latex_formula_global_optimum = r'\text{Global optimum: } x^* = unknown, F_1(x^*) = unknown'
continuous = True
linear = False
convex = False
unimodal = False
separable = False
differentiable = False
scalable = True
randomized_term = True
parametric = True
shifted = True
rotated = False
modality = True # Number of ambiguous peaks, unknown # peaks
# n_basins = 1
# n_valleys = 1
def __init__(self, ndim=None, bounds=None, f_shift="rastrigin_shift_func_data", f_bias=0.):
super().__init__()
self.dim_changeable = True
self.dim_default = 500
self.dim_max = 1000
self.check_ndim_and_bounds(ndim, self.dim_max, bounds, np.array([[-1., 1.] for _ in range(self.dim_default)]))
self.make_support_data_path("data_2008")
self.f_shift = self.check_shift_data(f_shift)[:self.ndim]
self.f_shift = self.f_shift / np.max(self.f_shift)
self.f_bias = f_bias
self.f_global = -1e32
self.x_global = self.f_shift
self.paras = {"f_shift": self.f_shift, "f_bias": self.f_bias}
[docs] def evaluate(self, x, *args):
self.n_fe += 1
self.check_solution(x, self.dim_max, self.dim_supported)
ndim = len(x)
results = [operator.fractal_1d_func(x[idx] + operator.twist_func(x[idx+1])) for idx in range(0, ndim-1)]
return np.sum(results) + operator.fractal_1d_func(x[-1] + operator.twist_func(x[0])) + self.f_bias