bbobbenchmarks.py 文件源码

def _evalfull(self, x):
        fadd = self.fopt
        curshape, dim = self.shape_(x)
        # it is assumed x are row vectors

        if self.lastshape != curshape:
            self.initwithsize(curshape, dim)

        # BOUNDARY HANDLING
        xoutside = np.maximum(0, np.abs(x) - 5.) * sign(x)
        fpen = 1e4 * np.sum(xoutside ** 2, -1)
        fadd = fadd + fpen

        # TRANSFORMATION IN SEARCH SPACE
        x = self.arrscales * x

        # COMPUTATION core
        s = 1 - .5 / ((dim + 20)**0.5 - 4.1) # tested up to DIM = 160 p in [0.25,0.33]
        d = 1 # shift [1,3], smaller is more difficult
        mu2 = -((self._mu1 ** 2 - d) / s) ** .5
        ftrue = np.minimum(np.sum((x - self._mu1) ** 2, -1),
                           d * dim + s * np.sum((x - mu2) ** 2, -1))
        ftrue = ftrue + 10 * (dim - np.sum(np.cos(2 * np.pi * dot(x - self._mu1, self.linearTF)), -1))
        fval = self.noise(ftrue)

        # FINALIZE
        ftrue += fadd
        fval += fadd
        return fval, ftrue

# dictbbob = {'sphere': F1, 'ellipsoid': F2, 'Rastrigin': F3}