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Global Optimization Toolbox

Simulated Annealing Solver

Simulated annealing solves optimization problems using a probabilistic search algorithm that mimics the physical process of annealing, in which a material is heated and then the temperature is slowly lowered to decrease defects, thus minimizing the system energy. By analogy, each iteration of a simulated annealing algorithm seeks to improve the current minimum by slowly reducing the extent of the search.

The simulated annealing 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 in early iterations and is able to explore globally for better solutions.

Simulated annealing allows you to solve unconstrained or bound-constrained optimization problems and does not require that the functions be differentiable or continuous. From the command line or Optimization app you can use toolbox functions to:

  • Solve problems using adaptive simulated annealing, Boltzmann annealing, or fast annealing algorithms
  • Create custom functions to define the annealing process, acceptance criteria, temperature schedule, plotting functions, simulation output, or custom data types
  • Perform hybrid optimization by specifying another optimization method to run at defined intervals or at normal solver termination
Using simulated annealing to solve a challenging problem that contains flat regions between basins.

Using simulated annealing to solve a challenging problem that contains flat regions between basins.

Next: Parallel Computing

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