Question

How do you perform global optimization using SciPy's differential_evolution function?

Answer 1

You can use the differential_evolution function from SciPy for global optimization. 

Here's an example:

from scipy.optimize import differential_evolution

# Define the objective function
def objective(x):
    return (x[0] - 2) ** 2 + (x[1] - 3) ** 2

# Define the bounds for the search space
bounds = [(0, 5), (0, 5)]

# Perform global optimization
result = differential_evolution(objective, bounds)

print("Global minimum value:", result.fun)
print("Optimal parameters:", result.x)