Why optim() always gives me the same result?

I'm trying to use bootstrapping to calculate the confidence interval of MLE of parameter v. But in every iteration, optim() gives me the same result. Why is this happenning?

Here's my code:

# Create log-likelihood function
dofloglik = function(v, x) { n <- length(x) 
                             loglik <- n*lgamma((v+1)/2) - n*lgamma(v/2)
                                       - (n/2)*log(v*pi) - ((v+1)/2)*sum(log(1+x^2/v))
                             return(-loglik) }

# Sample
y = c(0.97, 0.41, -0.13, -0.18, 1.69, 
       0.17, -0.46, -0.65, 2.66, -0.28, 
       2.30, -0.15, 0.24, 0.43, -0.54, 
       -1.55, 2.13, -0.48, 1.24, 1.05)

# Bootstrapping
n = length(y)
sims = 10
mle = numeric(sims)
for (i in 1:sims) { 
  bs = sample(y, n, replace=TRUE) 
  mle[i] = optim(1, dofloglik, x = bs, method = "CG")$par
  print(bs)
}
mle = sort(mle)
mle

The output is:

[1]  1.05  1.69 -0.13  2.66  2.13 -0.28 -0.54 -0.65  1.69  0.43 -0.18  0.17  1.24 -0.13 -1.55  0.41 -0.18  0.24 -0.54  2.13
[1] -0.13 -0.18  2.13 -1.55 -0.65 -0.28  1.24  2.13 -0.48  2.30 -0.28 -0.54 -0.13  0.41  1.24  1.24 -0.54 -0.18  2.66 -0.54
[1]  2.30  0.43 -1.55  1.05  2.13 -0.18  1.24  0.24 -0.13  2.30 -0.48  2.66 -0.13 -1.55 -0.54 -0.13  0.43 -0.13  0.24  2.30
[1]  1.24  1.24 -0.54  2.30 -0.46 -0.65  2.13  2.66 -0.46 -0.13 -0.18  2.66  0.24 -0.48 -1.55 -1.55  1.05  0.24  0.17 -0.46
[1]  0.97  2.66  2.13  0.24 -1.55  0.43  2.13  0.43 -0.18 -1.55 -0.46  1.69 -0.48  2.66 -0.18  2.30  2.66 -1.55 -0.54 -0.54
[1]  0.41  0.43  1.24  1.69 -0.13  0.41  1.69 -0.54 -0.28  2.13 -0.46  2.30  0.17  0.97  1.24  2.30  2.66  0.43  0.43  1.24
[1]  1.24  1.24  1.05  2.66  0.17  2.13  0.17 -0.13  2.30 -0.46 -1.55 -0.28  1.24  2.30 -0.48  0.24 -0.54  0.41 -0.65 -1.55
[1]  2.66 -0.54  1.05 -0.15  0.17 -1.55  0.41  1.69 -1.55 -1.55 -0.28 -0.46 -0.48 -0.13 -0.46  0.43  1.24  0.24 -0.46 -0.28
[1]  1.05  0.24  2.13  0.97  1.69  1.05  2.13 -0.15 -0.48 -1.55  1.05 -0.15  0.43 -0.13 -0.28  0.17  2.66 -0.15  1.24 -0.28
[1] -0.48 -0.18  0.24  2.30 -0.46 -0.54  0.43 -0.54  2.66 -0.48  2.66  0.24  2.13  0.97  1.05 -0.18  2.30 -0.13 -0.46  1.24
> mle = sort(mle)
> mle
[1] 47.16466 47.16466 47.16466 47.16466 47.16466 47.16466 47.16466 47.16466 47.16466 47.16466