so just using pymoo - I can't determine from the documentation how to pass parameters so that the evaluate function sees them:
import matplotlib.pyplot as plt
import numpy as np
from pymoo.algorithms.nsga2 import NSGA2
from pymoo.model.problem import Problem
from pymoo.optimize import minimize
from pymoo.visualization.scatter import Scatter
class MyProblem(Problem):
def __init__(self,*args, **kwargs):
"""
max f1 = X1 <br>
max f2 = 3 X1 + 4 X2 <br>
st X1 <= 20 <br>
X2 <= 40 <br>
5 X1 + 4 X2 <= 200 <br>
"""
print(args)
super().__init__(n_var=2,
n_obj=2,
n_constr=1,
xl=np.array([0, 0]),
xu=np.array([20, 40]))
def _evaluate(self, x, out, *args, **kwargs):
# define both objectives
f1 = x[:, 0]
f2 = 3 * x[:, 0] + 4 * x[:, 1]
# we have to negate the objectives because by default we assume minimization
f1, f2 = -f1, -f2
# define the constraint as a less or equal to zero constraint
g1 = 5 * x[:, 0] + 4 * x[:, 1] - 200
print(args)
out["F"] = np.column_stack([f1, f2])
out["G"] = g1
problem = MyProblem([1,2,3],[3,4,5])
algorithm = NSGA2()
res = minimize(problem,
algorithm,
('n_gen', 20000),
seed=1,
verbose=False)
#
# print(res.X)
# print(res.F)
# print(dir(res))
# print(res.opt)
print(res.F)
fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(12, 6))
Scatter(fig=fig, ax=ax1, title="Design Space").add(res.X, color="blue").do()
Scatter(fig=fig, ax=ax2, title="Objective Space").add(res.F, color="red").do()
plt.show()
I pass two lists to the Myproblem class, I do see them in the tuple args, but then when I look for these lists in args within evaluate it just gives me an empty tuple.
Suggestions?
