Python: fitting parameters with scipy differential_evolution, how to enforce one parameter be smaller than another one?

For example

from scipy.optimize import differential_evolution
import numpy as np

def func(parameters, *data):

    a,b,c = parameters
    x,y = data

    result = (a*x**2 + b*x+ c - y)**2

    return sum(result)

bounds = [(0.5, 1), (0.5, 1), (0.5, 1)]
x = np.array([1,2,3,4,5])
y = np.array([1,4,9,16,25])
args = (x,y)

parameters = differential_evolution(func, bounds, args=args,seed=np.random.seed(7))
print(parameters.x)

then a, b, c = [0.85699693 0.5 0.5]

Now, if some one want to enforce that c must be greater than a, i.e. c>a for the parameters fitting result. How could I achieve that?

My own solution is to use a conditional return for the defined function, so replace return sum(result) with return sum(result) if c>a else np.Inf. This gives me a new set of a, b, c = [0.83454878 0.50031474 0.83487768]

from scipy.optimize import differential_evolution
import numpy as np

def func(parameters, *data):

a,b,c = parameters
x,y = data

result = (a*x**2 + b*x+ c - y)**2

return sum(result) if c>a else np.Inf

bounds = [(0.5, 1), (0.5, 1), (0.5, 1)]
x = np.array([1,2,3,4,5])
y = np.array([1,4,9,16,25])
args = (x,y)

parameters = differential_evolution(func, bounds, args=args,seed=np.random.seed(7))
print(parameters.x)

[0.83454878 0.50031474 0.83487768]