Review:
Simulated Annealing
overall review score: 4.5
⭐⭐⭐⭐⭐
score is between 0 and 5
Simulated annealing is a probabilistic technique used for finding an approximate solution to optimization problems, inspired by the annealing process in metallurgy.
Key Features
- Temperature parameter controls randomness
- Transition probability function determines acceptance of new solutions
- Objective function evaluates quality of solutions
Pros
- Effective for finding near-optimal solutions in complex optimization problems
- Flexible and adaptable to various types of problems
- Can handle large search spaces with many local minima
Cons
- May require tuning of parameters for optimal performance
- Convergence may be slow for some problem instances