Cooling Schemes
===============

In the context of optimization algorithms—most notably Simulated Annealing—a **Cooling Scheme** (or cooling schedule)
is a mathematical strategy that dictates how the "temperature" parameter decreases over time.

Why Cooling Schemes are Important
---------------------------------

The temperature in an optimization process controls the balance between **exploration** and **exploitation**:

*   **Exploration (High Temperature):** Early in the process, high temperatures allow the algorithm to accept worse s
    olutions with a higher probability. This prevents the search from getting stuck in local optima and helps explore
    the broader search space.
*   **Exploitation (Low Temperature):** As the temperature decreases, the algorithm becomes more selective, eventually
    only accepting moves that improve the result. This allows the search to converge on a global optimum.

Choosing the right cooling scheme is critical because:

1.   **Convergence Speed:** If the temperature drops too quickly (quenched), the algorithm may converge prematurely to
     a sub-optimal local minimum.
2.   **Solution Quality:** If the temperature drops too slowly, the algorithm may take an impractical amount of time to
     reach a solution.
3.   **Problem Adaptability:** Different problem domains may require different cooling behaviors—such as linear,
     exponential, or adaptive adjustments—to find the best results efficiently.


.. include:: linear.rst

.. include:: exponential.rst

.. include:: logarithmic.rst

.. include:: cosine.rst

.. include:: adaptive.rst

.. include:: predefined.rst