Vectorized Matrix Operations

One way is using the NumPy matrix operations, which has been used for almost all test problems implemented in pymoo. By default, elementwise_evaluation is set to False, which implies the _evaluate retrieves a set of solutions. Thus, x is a matrix where each row is an individual, and each column a variable.

import numpy as np
from pymoo.core.problem import Problem

class MyProblem(Problem):

    def __init__(self, **kwargs):
        super().__init__(n_var=10, n_obj=1, n_ieq_constr=0, xl=-5, xu=5, **kwargs)

    def _evaluate(self, x, out, *args, **kwargs):
         out["F"] = np.sum(x ** 2, axis=1)

problem = MyProblem()

The axis=1 operation parallelizes the sum of the matrix directly using an efficient NumPy operation.

from pymoo.algorithms.soo.nonconvex.ga import GA
from pymoo.optimize import minimize

res = minimize(problem, GA(), termination=("n_gen", 200), seed=1)
print('Threads:', res.exec_time)

Threads: 0.4196128845214844