SciPy Differential Evolution: doesn't run on multiple cores even if workers = -1

Question

Context:

• I'm developing an optimizer by using SciPy's differential evolution package. I get some good results with worker = 1, but I would like to speed up the runtime.
• I checked the following thread already regarding How to enable parallel in scipy.optimize.differential_evolution? Even if I add if __name__ == "main": and set workers = -1, the runtime is exactly the same. I tested my code on my local machine (2 physical, 4 logical processors) or on our server environment (16 cores)
• I tested the following use case https://medium.com/@grvsinghal/speed-up-your-code-using-multiprocessing-in-python-36e4e703213e. Changing the number of workers does impact the runtime, so parallel processing work on my laptop and server as well.
• Consequently, my hypothesis is that the way I defined my objective and constraint function might be the problem.

Pseudo Code:

• The code is for work, so I can't share it. My objective and constraint functions need a lot of constants, hence I wrapped them into a Class. I know that the args parameter is for that, but the constraint function doesn't have that parameter.
• The code structure looks the following way:

class MyClass:

    def __init__(configuration, array1, array2, dataframe):
        # assigning attributes so that 
        self.something = datafame[column1]
        ...
   
    def obj(self, x):
        # based on the initialized values + optimized parameters, it calculates the objective

    def cons(self, x):
        # based on the initialized values + optimized parameters, it calculates the constraint violations

Then, I create a class instance o = MyClass() and call the differential evolution function with the class module: differential_evolution(func = o.obj, ...).

Question:

• Has anyone faced the following issue, i.e. even if you set multiple workers the code runs on one?
• Do you have any suggestions on how to better design the objective and constraint functions so that they are eligible for parallel processing?

Thank you!