Use of Simulated Annealing
Simulated annealing is well suited for problems where:
- The number of variables, and thus possible states, is very large.
This makes exhaustive search infeasible.
- There is ``frustration'' : it is not possible
to optimize all cost measures simultaneously.
- Significant improvements from a random starting position are
- There are many good near-optimal solutions.
- Hill-climbing is likely to get stuck on local maxima.
Computation is proportional to N or a small power of N ,
while finding the exact optimum is often NP-complete.