Add Y functions

opfunu/name_based/y_func.py

@@ -3,3 +3,54 @@
 #       Email: [email protected]            %                                                    
 #       Github: https://github.com/thieu1995        %                         
 # --------------------------------------------------%
+
+import numpy as np
+from opfunu.benchmark import Benchmark
+
+
+class YaoLiu04(Benchmark):
+    """
+    .. [1]  Yao X., Liu Y. (1997) Fast evolution strategies. In: Angeline P.J., Reynolds R.G., McDonnell J.R., Eberhart R. (eds)
+    Evolutionary Programming VI. EP 1997. Lecture Notes in Computer Science, vol 1213. Springer, Berlin, Heidelberg
+
+    .. [2]  Mishra, S. Global Optimization by Differential Evolution and Particle Swarm Methods: Evaluation
+    on Some Benchmark Functions. Munich Personal RePEc Archive, 2006, 1005
+
+    .. math::
+
+         f(x) = {max}_i \left\{ \left | x_i \right | , 1 \leq i \leq n \right\}
+
+    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) = 0` for :math:`x_i = 0` for :math:`i = 1, ..., n`
+    """
+    name = "Yao-Liu 4 Function"
+    latex_formula = r'f(x) = {max}_i \left\{ \left | x_i \right | , 1 \leq i \leq n \right\}'
+    latex_formula_dimension = r'd = n'
+    latex_formula_bounds = r'x_i \in [-10, 10, ..., 10]'
+    latex_formula_global_optimum = r'f(0, 0, ...,0) = 1.0'
+    continuous = True
+    linear = False
+    convex = True
+    unimodal = False
+    separable = True
+
+    differentiable = False
+    scalable = True
+    randomized_term = False
+    parametric = False
+
+    modality = False  # Number of ambiguous peaks, unknown # peaks
+
+    def __init__(self, ndim=None, bounds=None):
+        super().__init__()
+        self.dim_changeable = True
+        self.dim_default = 2
+        self.check_ndim_and_bounds(ndim, bounds, np.array([[-10., 10.] for _ in range(self.dim_default)]))
+        self.f_global = 0.
+        self.x_global = np.zeros(self.ndim)
+
+    def evaluate(self, x, *args):
+        self.check_solution(x)
+        self.n_fe += 1
+        return np.abs(x).max()