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Suppose I have a set of features(say 100 features) and spent a lot of time doing hyperparameter tuning to get a good model. Now I have a few new features(say less than 5 new features) added into the training set, should I re-do hyparameter tuning all over again? If so, is there any shortcut to get the optimal settings quickly for the new training set?

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In short: Yes, you should tune hyperparameters whenever you add features to a model.

By adding features you add extra dimensions for the algorithm to work with and also some noise. In my experience hyperparameters are quite dependent upon the number of features and what information contained in those features. Thus, re-tuning is advised. I don't know of any shortcuts.

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