579 lines
18 KiB
Python
579 lines
18 KiB
Python
# -*- coding: utf-8 -*-
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import numpy as np
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from numpy import (abs, asarray, cos, exp, floor, pi, sign, sin, sqrt, sum,
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size, tril, isnan, atleast_2d, repeat)
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from numpy.testing import assert_almost_equal
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from .go_benchmark import Benchmark
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class CarromTable(Benchmark):
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r"""
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CarromTable objective function.
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The CarromTable [1]_ global optimization problem is a multimodal
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minimization problem defined as follows:
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.. math::
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f_{\text{CarromTable}}(x) = - \frac{1}{30}\left(\cos(x_1)
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cos(x_2) e^{\left|1 - \frac{\sqrt{x_1^2 + x_2^2}}{\pi}\right|}\right)^2
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with :math:`x_i \in [-10, 10]` for :math:`i = 1, 2`.
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*Global optimum*: :math:`f(x) = -24.15681551650653` for :math:`x_i = \pm
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9.646157266348881` for :math:`i = 1, 2`
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.. [1] Jamil, M. & Yang, X.-S. A Literature Survey of Benchmark Functions
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For Global Optimization Problems Int. Journal of Mathematical Modelling
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and Numerical Optimisation, 2013, 4, 150-194.
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"""
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def __init__(self, dimensions=2):
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Benchmark.__init__(self, dimensions)
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self._bounds = list(zip([-10.0] * self.N, [10.0] * self.N))
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self.global_optimum = [(9.646157266348881, 9.646134286497169),
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(-9.646157266348881, 9.646134286497169),
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(9.646157266348881, -9.646134286497169),
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(-9.646157266348881, -9.646134286497169)]
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self.fglob = -24.15681551650653
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def fun(self, x, *args):
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self.nfev += 1
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u = cos(x[0]) * cos(x[1])
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v = sqrt(x[0] ** 2 + x[1] ** 2)
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return -((u * exp(abs(1 - v / pi))) ** 2) / 30.
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class Chichinadze(Benchmark):
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r"""
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Chichinadze objective function.
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This class defines the Chichinadze [1]_ global optimization problem. This is a
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multimodal minimization problem defined as follows:
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.. math::
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f_{\text{Chichinadze}}(x) = x_{1}^{2} - 12 x_{1}
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+ 8 \sin\left(\frac{5}{2} \pi x_{1}\right)
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+ 10 \cos\left(\frac{1}{2} \pi x_{1}\right) + 11
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- 0.2 \frac{\sqrt{5}}{e^{\frac{1}{2} \left(x_{2} -0.5 \right)^{2}}}
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with :math:`x_i \in [-30, 30]` for :math:`i = 1, 2`.
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*Global optimum*: :math:`f(x) = -42.94438701899098` for :math:`x =
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[6.189866586965680, 0.5]`
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.. [1] Gavana, A. Global Optimization Benchmarks and AMPGO retrieved 2015
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TODO: Jamil#33 has a dividing factor of 2 in the sin term. However, f(x)
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for the given solution does not give the global minimum. i.e. the equation
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is at odds with the solution.
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Only by removing the dividing factor of 2, i.e. `8 * sin(5 * pi * x[0])`
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does the given solution result in the given global minimum.
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Do we keep the result or equation?
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"""
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def __init__(self, dimensions=2):
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Benchmark.__init__(self, dimensions)
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self._bounds = list(zip([-30.0] * self.N, [30.0] * self.N))
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self.custom_bounds = [(-10, 10), (-10, 10)]
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self.global_optimum = [[6.189866586965680, 0.5]]
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self.fglob = -42.94438701899098
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def fun(self, x, *args):
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self.nfev += 1
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return (x[0] ** 2 - 12 * x[0] + 11 + 10 * cos(pi * x[0] / 2)
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+ 8 * sin(5 * pi * x[0] / 2)
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- 1.0 / sqrt(5) * exp(-((x[1] - 0.5) ** 2) / 2))
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class Cigar(Benchmark):
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r"""
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Cigar objective function.
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This class defines the Cigar [1]_ global optimization problem. This
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is a multimodal minimization problem defined as follows:
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.. math::
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f_{\text{Cigar}}(x) = x_1^2 + 10^6\sum_{i=2}^{n} x_i^2
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Here, :math:`n` represents the number of dimensions and :math:`x_i \in
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[-100, 100]` for :math:`i = 1, ..., n`.
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*Global optimum*: :math:`f(x) = 0` for :math:`x_i = 0` for
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:math:`i = 1, ..., n`
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.. [1] Gavana, A. Global Optimization Benchmarks and AMPGO retrieved 2015
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"""
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def __init__(self, dimensions=2):
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Benchmark.__init__(self, dimensions)
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self._bounds = list(zip([-100.0] * self.N,
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[100.0] * self.N))
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self.custom_bounds = [(-5, 5), (-5, 5)]
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self.global_optimum = [[0 for _ in range(self.N)]]
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self.fglob = 0.0
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self.change_dimensionality = True
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def fun(self, x, *args):
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self.nfev += 1
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return x[0] ** 2 + 1e6 * sum(x[1:] ** 2)
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class Cola(Benchmark):
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r"""
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Cola objective function.
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This class defines the Cola global optimization problem. The 17-dimensional
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function computes indirectly the formula :math:`f(n, u)` by setting
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:math:`x_0 = y_0, x_1 = u_0, x_i = u_{2(i2)}, y_i = u_{2(i2)+1}` :
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.. math::
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f_{\text{Cola}}(x) = \sum_{i<j}^{n} \left (r_{i,j} - d_{i,j} \right )^2
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Where :math:`r_{i, j}` is given by:
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.. math::
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r_{i, j} = \sqrt{(x_i - x_j)^2 + (y_i - y_j)^2}
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And :math:`d` is a symmetric matrix given by:
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.. math::
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\{d} = \left [ d_{ij} \right ] = \begin{pmatrix}
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1.27 & & & & & & & & \\
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1.69 & 1.43 & & & & & & & \\
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2.04 & 2.35 & 2.43 & & & & & & \\
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3.09 & 3.18 & 3.26 & 2.85 & & & & & \\
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3.20 & 3.22 & 3.27 & 2.88 & 1.55 & & & & \\
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2.86 & 2.56 & 2.58 & 2.59 & 3.12 & 3.06 & & & \\
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3.17 & 3.18 & 3.18 & 3.12 & 1.31 & 1.64 & 3.00 & \\
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3.21 & 3.18 & 3.18 & 3.17 & 1.70 & 1.36 & 2.95 & 1.32 & \\
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2.38 & 2.31 & 2.42 & 1.94 & 2.85 & 2.81 & 2.56 & 2.91 & 2.97
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\end{pmatrix}
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This function has bounds :math:`x_0 \in [0, 4]` and :math:`x_i \in [-4, 4]`
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for :math:`i = 1, ..., n-1`.
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*Global optimum* 11.7464.
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.. [1] Jamil, M. & Yang, X.-S. A Literature Survey of Benchmark Functions
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For Global Optimization Problems Int. Journal of Mathematical Modelling
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and Numerical Optimisation, 2013, 4, 150-194.
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"""
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def __init__(self, dimensions=17):
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Benchmark.__init__(self, dimensions)
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self._bounds = [[0.0, 4.0]] + list(zip([-4.0] * (self.N - 1),
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[4.0] * (self.N - 1)))
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self.global_optimum = [[0.651906, 1.30194, 0.099242, -0.883791,
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-0.8796, 0.204651, -3.28414, 0.851188,
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-3.46245, 2.53245, -0.895246, 1.40992,
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-3.07367, 1.96257, -2.97872, -0.807849,
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-1.68978]]
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self.fglob = 11.7464
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self.d = asarray([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[1.27, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[1.69, 1.43, 0, 0, 0, 0, 0, 0, 0, 0],
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[2.04, 2.35, 2.43, 0, 0, 0, 0, 0, 0, 0],
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[3.09, 3.18, 3.26, 2.85, 0, 0, 0, 0, 0, 0],
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[3.20, 3.22, 3.27, 2.88, 1.55, 0, 0, 0, 0, 0],
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[2.86, 2.56, 2.58, 2.59, 3.12, 3.06, 0, 0, 0, 0],
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[3.17, 3.18, 3.18, 3.12, 1.31, 1.64, 3.00, 0, 0, 0],
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[3.21, 3.18, 3.18, 3.17, 1.70, 1.36, 2.95, 1.32, 0, 0],
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[2.38, 2.31, 2.42, 1.94, 2.85, 2.81, 2.56, 2.91, 2.97, 0.]])
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def fun(self, x, *args):
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self.nfev += 1
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xi = atleast_2d(asarray([0.0, x[0]] + list(x[1::2])))
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xj = repeat(xi, size(xi, 1), axis=0)
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xi = xi.T
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yi = atleast_2d(asarray([0.0, 0.0] + list(x[2::2])))
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yj = repeat(yi, size(yi, 1), axis=0)
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yi = yi.T
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inner = (sqrt(((xi - xj) ** 2 + (yi - yj) ** 2)) - self.d) ** 2
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inner = tril(inner, -1)
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return sum(sum(inner, axis=1))
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class Colville(Benchmark):
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r"""
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Colville objective function.
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This class defines the Colville global optimization problem. This
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is a multimodal minimization problem defined as follows:
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.. math::
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f_{\text{Colville}}(x) = \left(x_{1} -1\right)^{2}
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+ 100 \left(x_{1}^{2} - x_{2}\right)^{2}
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+ 10.1 \left(x_{2} -1\right)^{2} + \left(x_{3} -1\right)^{2}
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+ 90 \left(x_{3}^{2} - x_{4}\right)^{2}
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+ 10.1 \left(x_{4} -1\right)^{2} + 19.8 \frac{x_{4} -1}{x_{2}}
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with :math:`x_i \in [-10, 10]` for :math:`i = 1, ..., 4`.
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*Global optimum*: :math:`f(x) = 0` for :math:`x_i = 1` for
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:math:`i = 1, ..., 4`
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.. [1] Jamil, M. & Yang, X.-S. A Literature Survey of Benchmark Functions
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For Global Optimization Problems Int. Journal of Mathematical Modelling
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and Numerical Optimisation, 2013, 4, 150-194.
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TODO docstring equation is wrong use Jamil#36
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"""
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def __init__(self, dimensions=4):
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Benchmark.__init__(self, dimensions)
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self._bounds = list(zip([-10.0] * self.N, [10.0] * self.N))
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self.global_optimum = [[1 for _ in range(self.N)]]
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self.fglob = 0.0
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def fun(self, x, *args):
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self.nfev += 1
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return (100 * (x[0] - x[1] ** 2) ** 2
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+ (1 - x[0]) ** 2 + (1 - x[2]) ** 2
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+ 90 * (x[3] - x[2] ** 2) ** 2
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+ 10.1 * ((x[1] - 1) ** 2 + (x[3] - 1) ** 2)
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+ 19.8 * (x[1] - 1) * (x[3] - 1))
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class Corana(Benchmark):
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r"""
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Corana objective function.
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This class defines the Corana [1]_ global optimization problem. This
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is a multimodal minimization problem defined as follows:
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.. math::
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f_{\text{Corana}}(x) = \begin{cases} \sum_{i=1}^n 0.15 d_i
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[z_i - 0.05\textrm{sgn}(z_i)]^2 & \textrm{if }|x_i-z_i| < 0.05 \\
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d_ix_i^2 & \textrm{otherwise}\end{cases}
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Where, in this exercise:
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.. math::
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z_i = 0.2 \lfloor |x_i/s_i|+0.49999\rfloor\textrm{sgn}(x_i),
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d_i=(1,1000,10,100, ...)
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with :math:`x_i \in [-5, 5]` for :math:`i = 1, ..., 4`.
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*Global optimum*: :math:`f(x) = 0` for :math:`x_i = 0` for
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:math:`i = 1, ..., 4`
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..[1] Gavana, A. Global Optimization Benchmarks and AMPGO retrieved 2015
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"""
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def __init__(self, dimensions=4):
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Benchmark.__init__(self, dimensions)
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self._bounds = list(zip([-5.0] * self.N, [5.0] * self.N))
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self.global_optimum = [[0 for _ in range(self.N)]]
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self.fglob = 0.0
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def fun(self, x, *args):
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self.nfev += 1
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d = [1., 1000., 10., 100.]
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r = 0
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for j in range(4):
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zj = floor(abs(x[j] / 0.2) + 0.49999) * sign(x[j]) * 0.2
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if abs(x[j] - zj) < 0.05:
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r += 0.15 * ((zj - 0.05 * sign(zj)) ** 2) * d[j]
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else:
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r += d[j] * x[j] * x[j]
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return r
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class CosineMixture(Benchmark):
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r"""
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Cosine Mixture objective function.
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This class defines the Cosine Mixture global optimization problem. This
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is a multimodal minimization problem defined as follows:
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.. math::
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f_{\text{CosineMixture}}(x) = -0.1 \sum_{i=1}^n \cos(5 \pi x_i)
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- \sum_{i=1}^n x_i^2
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Here, :math:`n` represents the number of dimensions and :math:`x_i \in
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[-1, 1]` for :math:`i = 1, ..., N`.
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*Global optimum*: :math:`f(x) = -0.1N` for :math:`x_i = 0` for
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:math:`i = 1, ..., N`
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.. [1] Jamil, M. & Yang, X.-S. A Literature Survey of Benchmark Functions
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For Global Optimization Problems Int. Journal of Mathematical Modelling
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and Numerical Optimisation, 2013, 4, 150-194.
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TODO, Jamil #38 has wrong minimum and wrong fglob. I plotted it.
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-(x**2) term is always negative if x is negative.
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cos(5 * pi * x) is equal to -1 for x=-1.
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"""
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def __init__(self, dimensions=2):
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Benchmark.__init__(self, dimensions)
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self.change_dimensionality = True
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self._bounds = list(zip([-1.0] * self.N, [1.0] * self.N))
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self.global_optimum = [[-1. for _ in range(self.N)]]
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self.fglob = -0.9 * self.N
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def fun(self, x, *args):
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self.nfev += 1
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return -0.1 * sum(cos(5.0 * pi * x)) - sum(x ** 2.0)
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class CrossInTray(Benchmark):
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r"""
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Cross-in-Tray objective function.
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This class defines the Cross-in-Tray [1]_ global optimization problem. This is a
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multimodal minimization problem defined as follows:
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.. math::
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f_{\text{CrossInTray}}(x) = - 0.0001 \left(\left|{e^{\left|{100
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- \frac{\sqrt{x_{1}^{2} + x_{2}^{2}}}{\pi}}\right|}
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\sin\left(x_{1}\right) \sin\left(x_{2}\right)}\right| + 1\right)^{0.1}
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with :math:`x_i \in [-15, 15]` for :math:`i = 1, 2`.
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*Global optimum*: :math:`f(x) = -2.062611870822739` for :math:`x_i =
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\pm 1.349406608602084` for :math:`i = 1, 2`
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.. [1] Jamil, M. & Yang, X.-S. A Literature Survey of Benchmark Functions
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For Global Optimization Problems Int. Journal of Mathematical Modelling
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and Numerical Optimisation, 2013, 4, 150-194.
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"""
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def __init__(self, dimensions=2):
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Benchmark.__init__(self, dimensions)
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self._bounds = list(zip([-10.0] * self.N, [10.0] * self.N))
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self.global_optimum = [(1.349406685353340, 1.349406608602084),
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(-1.349406685353340, 1.349406608602084),
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(1.349406685353340, -1.349406608602084),
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(-1.349406685353340, -1.349406608602084)]
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self.fglob = -2.062611870822739
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def fun(self, x, *args):
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self.nfev += 1
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return (-0.0001 * (abs(sin(x[0]) * sin(x[1])
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* exp(abs(100 - sqrt(x[0] ** 2 + x[1] ** 2) / pi)))
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+ 1) ** (0.1))
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class CrossLegTable(Benchmark):
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r"""
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Cross-Leg-Table objective function.
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This class defines the Cross-Leg-Table [1]_ global optimization problem. This
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is a multimodal minimization problem defined as follows:
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.. math::
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f_{\text{CrossLegTable}}(x) = - \frac{1}{\left(\left|{e^{\left|{100
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- \frac{\sqrt{x_{1}^{2} + x_{2}^{2}}}{\pi}}\right|}
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\sin\left(x_{1}\right) \sin\left(x_{2}\right)}\right| + 1\right)^{0.1}}
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with :math:`x_i \in [-10, 10]` for :math:`i = 1, 2`.
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*Global optimum*: :math:`f(x) = -1`. The global minimum is found on the
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planes :math:`x_1 = 0` and :math:`x_2 = 0`
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|
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..[1] Mishra, S. Global Optimization by Differential Evolution and Particle
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Swarm Methods: Evaluation on Some Benchmark Functions Munich University,
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2006
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"""
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def __init__(self, dimensions=2):
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Benchmark.__init__(self, dimensions)
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self._bounds = list(zip([-10.0] * self.N, [10.0] * self.N))
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self.global_optimum = [[0., 0.]]
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self.fglob = -1.0
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def fun(self, x, *args):
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self.nfev += 1
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u = 100 - sqrt(x[0] ** 2 + x[1] ** 2) / pi
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v = sin(x[0]) * sin(x[1])
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return -(abs(v * exp(abs(u))) + 1) ** (-0.1)
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class CrownedCross(Benchmark):
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r"""
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Crowned Cross objective function.
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This class defines the Crowned Cross [1]_ global optimization problem. This
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is a multimodal minimization problem defined as follows:
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.. math::
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f_{\text{CrownedCross}}(x) = 0.0001 \left(\left|{e^{\left|{100
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- \frac{\sqrt{x_{1}^{2} + x_{2}^{2}}}{\pi}}\right|}
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\sin\left(x_{1}\right) \sin\left(x_{2}\right)}\right| + 1\right)^{0.1}
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with :math:`x_i \in [-10, 10]` for :math:`i = 1, 2`.
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*Global optimum*: :math:`f(x_i) = 0.0001`. The global minimum is found on
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the planes :math:`x_1 = 0` and :math:`x_2 = 0`
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..[1] Mishra, S. Global Optimization by Differential Evolution and Particle
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Swarm Methods: Evaluation on Some Benchmark Functions Munich University,
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2006
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"""
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def __init__(self, dimensions=2):
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Benchmark.__init__(self, dimensions)
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self._bounds = list(zip([-10.0] * self.N, [10.0] * self.N))
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self.global_optimum = [[0, 0]]
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self.fglob = 0.0001
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def fun(self, x, *args):
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self.nfev += 1
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u = 100 - sqrt(x[0] ** 2 + x[1] ** 2) / pi
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v = sin(x[0]) * sin(x[1])
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return 0.0001 * (abs(v * exp(abs(u))) + 1) ** (0.1)
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class Csendes(Benchmark):
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r"""
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Csendes objective function.
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This class defines the Csendes [1]_ global optimization problem. This is a
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multimodal minimization problem defined as follows:
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.. math::
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f_{\text{Csendes}}(x) = \sum_{i=1}^n x_i^6 \left[ 2 + \sin
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\left( \frac{1}{x_i} \right ) \right]
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|
|
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Here, :math:`n` represents the number of dimensions and :math:`x_i \in
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[-1, 1]` for :math:`i = 1, ..., N`.
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|
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|
*Global optimum*: :math:`f(x) = 0.0` for :math:`x_i = 0` for
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|
:math:`i = 1, ..., N`
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|
|
|
.. [1] Jamil, M. & Yang, X.-S. A Literature Survey of Benchmark Functions
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|
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)
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|
|
|
self.change_dimensionality = True
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|
self._bounds = list(zip([-1.0] * self.N, [1.0] * self.N))
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|
|
|
self.global_optimum = [[0 for _ in range(self.N)]]
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|
self.fglob = np.nan
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|
|
|
def fun(self, x, *args):
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|
self.nfev += 1
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|
|
|
try:
|
|
return sum((x ** 6.0) * (2.0 + sin(1.0 / x)))
|
|
except ZeroDivisionError:
|
|
return np.nan
|
|
except FloatingPointError:
|
|
return np.nan
|
|
|
|
def success(self, x):
|
|
"""Is a candidate solution at the global minimum"""
|
|
val = self.fun(asarray(x))
|
|
if isnan(val):
|
|
return True
|
|
try:
|
|
assert_almost_equal(val, 0., 4)
|
|
return True
|
|
except AssertionError:
|
|
return False
|
|
|
|
return False
|
|
|
|
|
|
class Cube(Benchmark):
|
|
r"""
|
|
Cube objective function.
|
|
|
|
This class defines the Cube global optimization problem. This
|
|
is a multimodal minimization problem defined as follows:
|
|
|
|
.. math::
|
|
|
|
f_{\text{Cube}}(x) = 100(x_2 - x_1^3)^2 + (1 - x1)^2
|
|
|
|
|
|
Here, :math:`n` represents the number of dimensions and :math:`x_i \in [-10, 10]` for :math:`i=1,...,N`.
|
|
|
|
*Global optimum*: :math:`f(x_i) = 0.0` for :math:`x = [1, 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.
|
|
|
|
TODO: jamil#41 has the wrong solution.
|
|
"""
|
|
|
|
def __init__(self, dimensions=2):
|
|
Benchmark.__init__(self, dimensions)
|
|
|
|
self._bounds = list(zip([-10.0] * self.N, [10.0] * self.N))
|
|
self.custom_bounds = ([0, 2], [0, 2])
|
|
|
|
self.global_optimum = [[1.0, 1.0]]
|
|
self.fglob = 0.0
|
|
|
|
def fun(self, x, *args):
|
|
|
|
self.nfev += 1
|
|
return 100.0 * (x[1] - x[0] ** 3.0) ** 2.0 + (1.0 - x[0]) ** 2.0
|