What Is Simulated Annealing?
Simulated annealing is a method for solving unconstrained and
bound-constrained optimization problems. The method models the physical
process of heating a material and then slowly lowering the temperature
to decrease defects, thus minimizing the system energy.
At each iteration of the simulated annealing algorithm, a new
point is randomly generated. The distance of the new point from the
current point, or the extent of the search, is based on a probability
distribution with a scale proportional to the temperature. The algorithm
accepts all new points that lower the objective, but also, with a
certain probability, points that raise the objective. By accepting
points that raise the objective, the algorithm avoids being trapped
in local minima, and is able to explore globally for more possible
solutions. An annealing schedule is selected
to systematically decrease the temperature as the algorithm proceeds.
As the temperature decreases, the algorithm reduces the extent of
its search to converge to a minimum.
 | Getting Started with Simulated Annealing | | Performing a Simulated Annealing Optimization |  |
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