222 lines
6.3 KiB
Python
222 lines
6.3 KiB
Python
# -*- coding: utf-8 -*-
|
|
import numpy as np
|
|
from numpy import abs, sin, cos, exp, floor, log, arange, prod, sqrt, sum
|
|
|
|
from .go_benchmark import Benchmark
|
|
|
|
|
|
class Gear(Benchmark):
|
|
|
|
r"""
|
|
Gear objective function.
|
|
|
|
This class defines the Gear [1]_ global optimization problem. This
|
|
is a multimodal minimization problem defined as follows:
|
|
|
|
.. math::
|
|
|
|
f_{\text{Gear}}({x}) = \left \{ \frac{1.0}{6.931}
|
|
- \frac{\lfloor x_1\rfloor \lfloor x_2 \rfloor }
|
|
{\lfloor x_3 \rfloor \lfloor x_4 \rfloor } \right\}^2
|
|
|
|
|
|
with :math:`x_i \in [12, 60]` for :math:`i = 1, ..., 4`.
|
|
|
|
*Global optimum*: :math:`f(x) = 2.7 \cdot 10^{-12}` for :math:`x =
|
|
[16, 19, 43, 49]`, where the various :math:`x_i` may be permuted.
|
|
|
|
.. [1] Gavana, A. Global Optimization Benchmarks and AMPGO retrieved 2015
|
|
"""
|
|
|
|
def __init__(self, dimensions=4):
|
|
Benchmark.__init__(self, dimensions)
|
|
|
|
self._bounds = list(zip([12.0] * self.N, [60.0] * self.N))
|
|
self.global_optimum = [[16, 19, 43, 49]]
|
|
self.fglob = 2.7e-12
|
|
|
|
def fun(self, x, *args):
|
|
self.nfev += 1
|
|
|
|
return (1. / 6.931
|
|
- floor(x[0]) * floor(x[1]) / floor(x[2]) / floor(x[3])) ** 2
|
|
|
|
|
|
class Giunta(Benchmark):
|
|
|
|
r"""
|
|
Giunta objective function.
|
|
|
|
This class defines the Giunta [1]_ global optimization problem. This
|
|
is a multimodal minimization problem defined as follows:
|
|
|
|
.. math::
|
|
|
|
f_{\text{Giunta}}({x}) = 0.6 + \sum_{i=1}^{n} \left[\sin^{2}\left(1
|
|
- \frac{16}{15} x_i\right) - \frac{1}{50} \sin\left(4
|
|
- \frac{64}{15} x_i\right) - \sin\left(1
|
|
- \frac{16}{15} x_i\right)\right]
|
|
|
|
|
|
with :math:`x_i \in [-1, 1]` for :math:`i = 1, 2`.
|
|
|
|
*Global optimum*: :math:`f(x) = 0.06447042053690566` for
|
|
:math:`x = [0.4673200277395354, 0.4673200169591304]`
|
|
|
|
.. [1] Jamil, M. & Yang, X.-S. A Literature Survey of Benchmark Functions
|
|
For Global Optimization Problems Int. Journal of Mathematical Modelling
|
|
and Numerical Optimisation, 2013, 4, 150-194.
|
|
|
|
TODO Jamil has the wrong fglob. I think there is a lower value.
|
|
"""
|
|
|
|
def __init__(self, dimensions=2):
|
|
Benchmark.__init__(self, dimensions)
|
|
|
|
self._bounds = list(zip([-1.0] * self.N, [1.0] * self.N))
|
|
|
|
self.global_optimum = [[0.4673200277395354, 0.4673200169591304]]
|
|
self.fglob = 0.06447042053690566
|
|
|
|
def fun(self, x, *args):
|
|
self.nfev += 1
|
|
|
|
arg = 16 * x / 15.0 - 1
|
|
return 0.6 + sum(sin(arg) + sin(arg) ** 2 + sin(4 * arg) / 50.)
|
|
|
|
|
|
class GoldsteinPrice(Benchmark):
|
|
|
|
r"""
|
|
Goldstein-Price objective function.
|
|
|
|
This class defines the Goldstein-Price [1]_ global optimization problem. This
|
|
is a multimodal minimization problem defined as follows:
|
|
|
|
.. math::
|
|
|
|
f_{\text{GoldsteinPrice}}(x) = \left[ 1 + (x_1 + x_2 + 1)^2
|
|
(19 - 14 x_1 + 3 x_1^2 - 14 x_2 + 6 x_1 x_2 + 3 x_2^2) \right]
|
|
\left[ 30 + ( 2x_1 - 3 x_2)^2 (18 - 32 x_1 + 12 x_1^2
|
|
+ 48 x_2 - 36 x_1 x_2 + 27 x_2^2) \right]
|
|
|
|
|
|
with :math:`x_i \in [-2, 2]` for :math:`i = 1, 2`.
|
|
|
|
*Global optimum*: :math:`f(x) = 3` for :math:`x = [0, -1]`
|
|
|
|
.. [1] Jamil, M. & Yang, X.-S. A Literature Survey of Benchmark Functions
|
|
For Global Optimization Problems Int. Journal of Mathematical Modelling
|
|
and Numerical Optimisation, 2013, 4, 150-194.
|
|
"""
|
|
|
|
def __init__(self, dimensions=2):
|
|
Benchmark.__init__(self, dimensions)
|
|
|
|
self._bounds = list(zip([-2.0] * self.N, [2.0] * self.N))
|
|
|
|
self.global_optimum = [[0., -1.]]
|
|
self.fglob = 3.0
|
|
|
|
def fun(self, x, *args):
|
|
self.nfev += 1
|
|
|
|
a = (1 + (x[0] + x[1] + 1) ** 2
|
|
* (19 - 14 * x[0] + 3 * x[0] ** 2
|
|
- 14 * x[1] + 6 * x[0] * x[1] + 3 * x[1] ** 2))
|
|
b = (30 + (2 * x[0] - 3 * x[1]) ** 2
|
|
* (18 - 32 * x[0] + 12 * x[0] ** 2
|
|
+ 48 * x[1] - 36 * x[0] * x[1] + 27 * x[1] ** 2))
|
|
return a * b
|
|
|
|
|
|
class Griewank(Benchmark):
|
|
|
|
r"""
|
|
Griewank objective function.
|
|
|
|
This class defines the Griewank global optimization problem. This
|
|
is a multimodal minimization problem defined as follows:
|
|
|
|
.. math::
|
|
|
|
f_{\text{Griewank}}(x) = \frac{1}{4000}\sum_{i=1}^n x_i^2
|
|
- \prod_{i=1}^n\cos\left(\frac{x_i}{\sqrt{i}}\right) + 1
|
|
|
|
|
|
Here, :math:`n` represents the number of dimensions and
|
|
:math:`x_i \in [-600, 600]` for :math:`i = 1, ..., n`.
|
|
|
|
*Global optimum*: :math:`f(x) = 0` for :math:`x_i = 0` for
|
|
:math:`i = 1, ..., n`
|
|
|
|
.. [1] Jamil, M. & Yang, X.-S. A Literature Survey of Benchmark Functions
|
|
For Global Optimization Problems Int. Journal of Mathematical Modelling
|
|
and Numerical Optimisation, 2013, 4, 150-194.
|
|
"""
|
|
|
|
def __init__(self, dimensions=2):
|
|
Benchmark.__init__(self, dimensions)
|
|
|
|
self._bounds = list(zip([-100.0] * self.N,
|
|
[100.0] * self.N))
|
|
self.custom_bounds = [(-50, 50), (-50, 50)]
|
|
|
|
self.global_optimum = [[0 for _ in range(self.N)]]
|
|
self.fglob = 0.0
|
|
self.change_dimensionality = True
|
|
|
|
def fun(self, x, *args):
|
|
self.nfev += 1
|
|
|
|
i = arange(1., np.size(x) + 1.)
|
|
return sum(x ** 2 / 4000) - prod(cos(x / sqrt(i))) + 1
|
|
|
|
|
|
class Gulf(Benchmark):
|
|
|
|
r"""
|
|
Gulf objective function.
|
|
|
|
This class defines the Gulf [1]_ global optimization problem. This is a
|
|
multimodal minimization problem defined as follows:
|
|
|
|
.. math::
|
|
|
|
f_{\text{Gulf}}(x) = \sum_{i=1}^99 \left( e^{-\frac{\lvert y_i
|
|
- x_2 \rvert^{x_3}}{x_1}} - t_i \right)
|
|
|
|
|
|
Where, in this exercise:
|
|
|
|
.. math::
|
|
|
|
t_i = i/100 \\
|
|
y_i = 25 + [-50 \log(t_i)]^{2/3}
|
|
|
|
|
|
with :math:`x_i \in [0, 60]` for :math:`i = 1, 2, 3`.
|
|
|
|
*Global optimum*: :math:`f(x) = 0` for :math:`x = [50, 25, 1.5]`
|
|
|
|
.. [1] Gavana, A. Global Optimization Benchmarks and AMPGO retrieved 2015
|
|
|
|
TODO Gavana has absolute of (u - x[1]) term. Jamil doesn't... Leaving it in.
|
|
"""
|
|
|
|
def __init__(self, dimensions=3):
|
|
Benchmark.__init__(self, dimensions)
|
|
|
|
self._bounds = list(zip([0.0] * self.N, [50.0] * self.N))
|
|
|
|
self.global_optimum = [[50.0, 25.0, 1.5]]
|
|
self.fglob = 0.0
|
|
|
|
def fun(self, x, *args):
|
|
self.nfev += 1
|
|
|
|
m = 99.
|
|
i = arange(1., m + 1)
|
|
u = 25 + (-50 * log(i / 100.)) ** (2 / 3.)
|
|
vec = (exp(-((abs(u - x[1])) ** x[2] / x[0])) - i / 100.)
|
|
return sum(vec ** 2)
|