Best current tools for Multi-Objective Surrogate-Based Optimization (MOSBO) on heterogeneous study data meta-analysis?[P]

I'm working on a project with summarized data from ~40 studies (Excel) involving different protocol variables (durations, intensities, recovery times, frequency, total duration, etc.) and response outcomes conditional on a baseline variable (range ~30-85 units).

The aim is to fit a continuous response surface using a hierarchical approach to separate protocol effects from baseline effects, then perform continuous numerical optimization (not grid search) for three objectives:
- Total improvement
- Improvement per unit time (e.g. per week)
- Improvement per unit effort/work

Outputs should be fine-grained continuous values rather than rounded study parameters. There are also domain-specific physiological constraints to respect.

I'm on a Chromebook with a little Python experience, so Colab-friendly solutions would be ideal.

Current candidates I'm considering: PyMC for hierarchical modeling, pymoo + pysamoo for surrogate-assisted MO optimization, SMT for surrogates, or Matlab Global Optimization Toolbox.

What is the strongest stack in 2026 for this kind of workflow? Any recommended notebooks, tutorials, or similar applied examples?

Are there any AI tools that currently do this without the traditional work of python? Meaning I can upload the spreadsheet give a parameters and it will come up with data.?