# How to find Global Optima using Simulated Annealing in Python

In Python, you can use the `scipy.optimize.dual_annealing` function to perform global optimization using Simulated Annealing. This optimization method is particularly useful for finding global optima in complex, non-convex, and multi-modal objective functions.

Here’s an example of how to use Simulated Annealing for global optimization:

``````from scipy.optimize import dual_annealing

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

# Define the bounds for the variables
bounds = [(-5, 5), (-5, 5)]  # Adjust bounds based on your problem

# Perform global optimization using Simulated Annealing
result = dual_annealing(objective_function, bounds)

# Display the result
print("Global optimum:", result.x)
print("Optimal objective function value:", result.fun)
``````

In this example:

• `objective_function` is the function you want to minimize.
• `bounds` specify the bounds for each variable in the objective function.

`dual_annealing` uses Simulated Annealing to find the global minimum of the objective function within the specified bounds. You can adjust the bounds and the objective function based on your specific problem.

Always refer to the SciPy documentation for detailed information on the parameters and options available for `dual_annealing`. Additionally, experiment with the `maxiter` and `local_search_options` parameters to fine-tune the algorithm’s behavior according to your optimization requirements.