Pymoo learning (8):grades

1 Gradients

   If the optimization problem is used autograd Realization , So you can go through AutomaticDifferentiation To deal with it ：

import autograd.numpy as anp
from pymoo.core.problem import Problem
from pymoo.problems.autodiff import AutomaticDifferentiation


class MyProblem(Problem):

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

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


if __name__ == '__main__':
    problem = AutomaticDifferentiation(MyProblem())

    X = anp.array([anp.arange(10)])
    F, dF = problem.evaluate(X, return_values_of=["F", "dF"])

    print(X, F)
    print(dF.shape)
    print(dF)

   Output is as follows ：

[[0 1 2 3 4 5 6 7 8 9]] [[285.]]
(1, 1, 10)
[[[ 0.  2.  4.  6.  8. 10. 12. 14. 16. 18.]]]

reference

【1】https://pymoo.org/problems/gradients.html