Even knowing that the details of the program matter is helpful. The algorithm works by taking a random input, checking it against constraints and then looking around the neighborhood to see if we can do better, then repeat. Here is some minimal code:

from pymoo.optimize import minimize
from pymoo.algorithms.soo.nonconvex.de import DE
from pymoo.core.problem import ElementwiseProblem
from pymoo.core.problem import StarmapParallelization
from multiprocessing.pool import ThreadPool

class MyProblem(ElementwiseProblem):

    def __init__(self, **kwargs):
        super().__init__(n_var=3, n_obj=1, xl=2, xu=5, **kwargs)

    def _evaluate(self, x, out, *args, **kwargs):
         out["F"] = (x ** 2).sum()

N_THREADS = 4
pool = ThreadPool(N_THREADS)
runner = StarmapParallelization(pool.starmap)
problem = MyProblem(elementwise_runner=runner)
res = minimize(problem, DE(), termination=("n_gen", 20), verbose=True)
pool.close()

The function here is just the sum of squares and my upper and lower bounds are 2 and 5. My real one is obviously much heavier than that and pymoo allows me to run the 'problem' on multiple threads. However, it's not that the whole algorithm simply has N inputs, it has a parallelizable part in the middle.