Why bias is not considering in Regularization?
The point of regularization is to avoid overfitting, and overfitting happens when you have too many predictor variables (i.e. neurons) contributing to the outcome. So, by regularization you are essentially excluding some neurons.

However, bias is an intrinsic property of every neuron. It is not a connection transporting the result of a neuron from the previous layer. Maybe you can imagine all biases in the network being connections to a single, virtual neuron with a constant output of one, but still, as it is a constant, it is not a result of a computation and does not depend on the input.