Fastanneal

Fastanneal is a class of optimization techniques designed to speed up the annealing phase used in simulated annealing and its variants. It leverages the probabilistic search framework of simulated annealing to find near-optimal solutions more quickly by employing faster cooling schedules, adaptive temperature control, or parallel exploration, while maintaining the ability to escape local minima.

In a typical fastanneal procedure, the problem is framed as minimizing an objective function treated as an energy. A current solution generates a neighboring candidate; the change in energy ΔE is computed; the candidate is accepted with the Metropolis probability exp(-ΔE/T) when ΔE > 0 or by default when ΔE ≤ 0. The temperature T is reduced according to a fast or adaptive cooling schedule. Variants may use non-monotonic or problem-aware schedules, dynamic adjustment based on search progress, or parallel tempering to explore multiple temperatures simultaneously.

The main advantages of fastanneal are reduced computation time and better scalability to large problem instances. It can perform well on combinatorial optimization problems such as routing, scheduling, and layout design when the cooling parameters are well tuned. The main drawbacks include a higher risk of converging to suboptimal solutions if the cooling is too aggressive or the schedule is poorly chosen, and greater sensitivity to the choice of neighborhood structure and initial temperature.

Historically, fastanneal is not a single standardized algorithm, but a family of accelerated annealing strategies that extend the core ideas of simulated annealing. It is commonly implemented as an option or module within optimization libraries, alongside other heuristics such as genetic algorithms, tabu search, and parallel tempering.