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)
