I will cite the FAQ from t-SNE website.
First for perplexity:
How should I set the perplexity in t-SNE?
The performance of t-SNE is fairly robust under different settings of
the perplexity. The most appropriate value depends on the density of
your data. Loosely speaking, one could say that a larger / denser
dataset requires a larger perplexity. Typical values for the
perplexity range between 5 and 50.
For all other paremeters I would consider reading this:
How can I asses the quality of the visualizations that t-SNE
Preferably, just look at them! Notice that t-SNE does not retain
distances but probabilities, so measuring some error between the
Euclidean distances in high-D and low-D is useless. However, if you
use the same data and perplexity, you can compare the Kullback-Leibler
divergences that t-SNE reports. It is perfectly fine to run t-SNE ten
times, and select the solution with the lowest KL divergence.
In other words it means: look at the plot, if the visualization is good don't change the parameters. You can also choose the run with the lowest KL divergence for each fixed perplexity.