Improving Stockfish computation to generate best possible move

Question

I use the stockfish engine to generate the optimal moves in an simulated chess game. I use python-chess to integrate the Stockfish engine in my simulation. At the moment I set the depth of the search operation for the optimal move with chess.engine.Limit(depth=engine_depth)

Stockfish 11 is stated to have an approximated rating of about 3550.

Of course the quality of the move depends on the search depth. In this paper (http://web.ist.utl.pt/diogo.ferreira/papers/ferreira13impact.pdf) they analyze the correlation between search depth and rating of the engine the essential result is basically: Unfortunately that paper is from 2013 and no specific engine is named, but it can be assumed that results are nowadays a little higher. I also found this stack overflow article: Is the depth of a chess engine its strength? but most of the links there are dead.

I want to look deeper into how to achieve the optimal move. Because when using Stockfish 11 with depth 20 it can barely win against the computer of chess.com level 10 which is rumored to have a rating of around 2600. Most often Stockfish achieves a draw and can win sometimes with white.

This lets me to believe that just given Stockfish the search depth is not the best way. Aspects like search-tree pruning etc. could drastically improve the search results for the optimal move.

So has anybody experience with Stockfish and can guide me in the correct direction on how to optimize the search for the best possible move. Ideally, you could provide some code for python-chess with which I'm using right now.