Tuning Random Forest parameters with an optimization strategy (PSO) other than Bayesian Optimization

Hi! I’m exploring different approach of tuning the Random forest parameters. I started with the Bayesian Optimization approach (https://ch.mathworks.com/help/stats/tune-random-forest-using-quantile-error-and-bayesian-optimization.html), which works perfectly fine.

Then, I tried to repeat the same procedure, but this time, with the PSO optimization algorithm. In this other case, I want to keep using the out-of-bag error function ‘oobErrRF’ (used in the MATLAB example of Bayesian optimization----See the above link) as ‘function to be minimized’.

1. I can get the code to work, especially due to the error “Undefined function or variable 'params'.”
2. I suspected that this error is due to “Calling an object method without an object as the first input” (https://ch.mathworks.com/matlabcentral/answers/96005-why-do-i-get-the-error-undefined-function-or-variable).
3. But still, I failed to solve this issue. the “params” here seems to be specific (embedded) within the error function and the Bayesian optimization paradigm? How can I solve this problem?
4. How can I define (extract) the “params” for a "PSO optimization" while keep using the ‘oobErrRF’ function?

The code is provided for a clearer understanding of my problem. Thank you for your precious help.