93 lines
2.8 KiB
Python
93 lines
2.8 KiB
Python
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# -*- coding: utf-8 -*-
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from numpy import abs, sum, cos, pi
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from .go_benchmark import Benchmark
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class YaoLiu04(Benchmark):
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r"""
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Yao-Liu 4 objective function.
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This class defines the Yao-Liu function 4 [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{YaoLiu04}}(x) = {max}_i \left\{ \left | x_i \right | ,
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1 \leq i \leq n \right\}
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Here, :math:`n` represents the number of dimensions and
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:math:`x_i \in [-10, 10]` 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] Yao X., Liu Y. (1997) Fast evolution strategies.
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In: Angeline P.J., Reynolds R.G., McDonnell J.R., Eberhart R. (eds)
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Evolutionary Programming VI. EP 1997.
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Lecture Notes in Computer Science, vol 1213. Springer, Berlin, Heidelberg
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.. [2] Mishra, S. Global Optimization by Differential Evolution and
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Particle Swarm Methods: Evaluation on Some Benchmark Functions.
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Munich Personal RePEc Archive, 2006, 1005
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TODO line 1201. Gavana code and documentation differ.
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max(abs(x)) != abs(max(x))
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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 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 abs(x).max()
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class YaoLiu09(Benchmark):
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r"""
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Yao-Liu 9 objective function.
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This class defines the Yao-Liu [1]_ function 9 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{YaoLiu09}}(x) = \sum_{i=1}^n \left [ x_i^2
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- 10 \cos(2 \pi x_i ) + 10 \right ]
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Here, :math:`n` represents the number of dimensions and
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:math:`x_i \in [-5.12, 5.12]` 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] Yao X., Liu Y. (1997) Fast evolution strategies.
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In: Angeline P.J., Reynolds R.G., McDonnell J.R., Eberhart R. (eds)
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Evolutionary Programming VI. EP 1997.
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Lecture Notes in Computer Science, vol 1213. Springer, Berlin, Heidelberg
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.. [2] 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([-5.12] * self.N, [5.12] * 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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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 sum(x ** 2.0 - 10.0 * cos(2 * pi * x) + 10)
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