def __init__(self, dim=10): self.xlow = -5 * np.ones(dim) self.xup = 5 * np.ones(dim) self.dim = dim self.info = str(dim)+"-dimensional Levy function \n" +\ "Global optimum: ?" self.integer = [] self.continuous = np.arange(0, dim) check_opt_prob(self)
def __init__(self, dim=10): self.xlow = -5.12 * np.ones(dim) self.xup = 5.12 * np.ones(dim) self.dim = dim self.info = str(dim)+"-dimensional Rastrigin function \n" +\ "Global optimum: f(0,0,...,0) = 0" self.min = 0 self.integer = [] self.continuous = np.arange(0, dim) check_opt_prob(self)
def __init__(self, dim=10): self.xlow = np.zeros(dim) self.xup = 5 * np.ones(dim) self.dim = dim self.min = -0.835 self.integer = [] self.continuous = np.arange(0, dim) self.info = str(dim)+"-dimensional Keane bump function \n" +\ "Global optimum: -0.835 for large n" check_opt_prob(self)
def __init__(self, dim=10): self.xlow = -10.24 * np.ones(dim) self.xup = 10.24 * np.ones(dim) self.dim = dim self.info = str(dim)+"-dimensional Whitley function \n" +\ "Global optimum: f(1,1,...,1) = 0" self.min = 0 self.integer = [] self.continuous = np.arange(0, dim) check_opt_prob(self)
def __init__(self, dim=3): self.xlow = np.zeros(3) self.xup = np.ones(3) self.dim = 3 self.info = "3-dimensional Hartman function \nGlobal optimum: " +\ "f(0.114614,0.555649,0.852547) = -3.86278" self.min = -3.86278 self.integer = [] self.continuous = np.arange(0, 3) check_opt_prob(self)
def __init__(self, dim=10): self.xlow = -512 * np.ones(dim) self.xup = 512 * np.ones(dim) self.dim = dim self.info = str(dim)+"-dimensional Schwefel function \n" +\ "Global optimum: f(420.968746,...,420.968746) = 0" self.min = 0 self.integer = [] self.continuous = np.arange(0, dim) check_opt_prob(self)
def __init__(self, dim=10): self.xlow = np.zeros(dim) self.xup = np.pi * np.ones(dim) self.dim = dim self.info = str(dim)+"-dimensional Michalewicz function \n" +\ "Global optimum: ??" self.min = np.NaN self.integer = [] self.continuous = np.arange(0, dim) check_opt_prob(self)
def __init__(self): self.xlow = np.zeros(5) self.xup = np.array([10, 10, 10, 1, 1]) self.dim = 5 self.min = -1 self.integer = np.arange(0, 3) self.continuous = np.arange(3, 5) self.info = str(self.dim)+"-dimensional Linear MI \n" +\ "Global optimum: f(1,0,0,0,0) = -1\n" +\ str(len(self.integer)) + " integer variables" check_opt_prob(self)
def __init__(self, dim=10): self.xlow = -5 * np.ones(dim) self.xup = 5 * np.ones(dim) self.dim = dim self.info = str(dim)+"-dimensional Styblinski-Tang function \n" +\ "Global optimum: f(-2.903534,...,-2.903534) = " +\ str(-39.16599*dim) self.min = -39.16599*dim self.integer = [] self.continuous = np.arange(0, dim) check_opt_prob(self)
def __init__(self, dim=10): self.xlow = -1.28 * np.ones(dim) self.xup = 1.28 * np.ones(dim) self.dim = dim self.info = str(dim)+"-dimensional Quartic function \n" +\ "Global optimum: f(0,0,...,0) = 0+noise" self.min = 0 self.integer = [] self.continuous = np.arange(0, dim) self.prng = random.Random() self.prng.seed(time()) check_opt_prob(self)