  In this work, we update and enlarge the long gamma-ray burst (GRB) sample
detected by the {\it Swift} satellite. Given the incomplete sampling of the
faint bursts and the low completeness in redshift measurement, we carefully
select a subsample of bright {\it Swift} bursts to revisit the GRB luminosity
function (LF) and redshift distribution by taking into account the probability
of redshift measurement. Here we also explore two general expressions for the
GRB LF, i.e., a broken power-law LF and a triple power-law LF. Our results
suggest that a strong redshift evolution in luminosity (with an evolution index
of $\delta=1.92^{+0.25}_{-0.37}$) or in density ($\delta=1.26^{+0.33}_{-0.34}$)
is required in order to well account for the observations, independent of the
assumed expression of the GRB LF. However, in a one-on-one comparison using the
Akaike information criterion, the best-fitting evolution model involving the
triple power-law LF is statistically preferred over the best-fitting one
involving the broken power-law LF with a relative probability of $\sim94.3$\%
versus $\sim5.7$\%. Extrapolating our fitting results to the flux limit of the
whole {\it Swift} sample, and considering the trigger probability of {\it
Swift}/Burst Alert Telescope in detail, we find that the expectations from our
evolution models provide a good representation of the observed distributions of
the whole sample without the need for any adjustment of the model free
parameters. This further confirms the reliability of our analysis results.
