constraint does not work

[faranak1991]

Hello,

I am using the following code to ensure that the offspring x data set satisfies this condition (by define a constraint) :
abs.(3 * x[0] + 1* x[1] + 1* x[2] + 3* x[3] + 3* x[4] + 3* x[5] + 3* x[6] + 6* x[7] + 6* x[8] + 6* x[9] + 9* x[10] ) < 2e-2

However, after tracking the output, I noticed that the individual's parameters x are not satisfying this condition.

Any help to identify and resolve the issue would be greatly appreciated ?

class myProblem(Problem):
def init(self, n_var, lower_bounds, upper_bounds):
super().init(n_var=n_var, n_obj=5, n_ieq_constr=1, xl=lower_bounds, xu=upper_bounds)

def _evaluate(self, X, out, *args, **kwargs):
    n_samples = X.shape[0]
    f1 = np.empty((n_samples, 1))
    f2 = np.empty((n_samples, 1))
    f3 = np.empty((n_samples, 1))
    f4 = np.empty((n_samples, 1))
    f5 = np.empty((n_samples, 1))
    g = np.empty((n_samples, 1))  

    for i in range(n_samples):
        x = X[i, :]  
        objectives = objective_function(x)  

        f1[i, 0] = objectives[0]  
        f2[i, 0] = objectives[1]   
        f3[i, 0] = objectives[2]   
        f4[i, 0] = objectives[3]   
        f5[i, 0] = objectives[4]   

        factors = np.array([3, 1, 1, 3, 3, 3, 3, 6, 6, 6, 9])
        constraint_value = np.abs(np.dot(factors, x))
        constraint_violation = constraint_value - 2e-2
        g[i, 0] = constraint_violation

    out["F"] = np.column_stack([f1, f2, f3, f4, f5])
    out["G"] = g

[blankjul]

I am not sure about your objective function but in fact the constraint violation can be satisfied.

import numpy as np

from pymoo.algorithms.soo.nonconvex.ga import GA
from pymoo.core.problem import Problem
from pymoo.optimize import minimize


class MyProblem(Problem):

    def __init__(self):
        super().__init__(n_var=11, n_obj=1, n_ieq_constr=1, xl=0, xu=1)

    def _evaluate(self, X, out, *args, **kwargs):
        n = X.shape[0]
        f = np.empty((n, 1))
        g = np.empty((n, 1))

        for i, x in enumerate(X):
            x = X[i, :]
            f[i, 0] = 0

            factors = np.array([3, 1, 1, 3, 3, 3, 3, 6, 6, 6, 9])
            constraint_value = np.abs(np.dot(factors, x))
            constraint_violation = constraint_value - 2e-2
            g[i, 0] = constraint_violation

        out["F"] = f
        out["G"] = g


problem = MyProblem()

algorithm = GA(pop_size=100, eliminate_duplicates=True)

res = minimize(problem,
               algorithm,
               seed=1,
               verbose=True)

print("Best solution found: \nX = %s\nF = %s\nCV = %s" % (res.X, res.F, res.CV))

[faranak1991]

Thanks for your reply, it works.