feat(algorithms): add SimulatedAnnealing single-objective local search

Classic Kirkpatrick et al. 1983 SA: hill climber that also accepts
worse moves with probability `exp(-Δ/T)` where T anneals geometrically
from `initial_temperature` to `final_temperature` over the iteration
count.

Single-objective only. Generic over decision type — works on real
vectors, bool vectors, permutations, anything. Tracks the best-seen
incumbent across the run (not just the last accepted move) so the
result reflects the actual best ever visited, not where the random
walk happened to end.

Tests cover: convergence on Sphere1D under reasonable hyperparameters,
deterministic reruns, panic on multi-objective, panic on
non-positive temperatures.

src/algorithms/mod.rs

@@ -8,6 +8,7 @@ pub mod nsga3;
 pub mod paes;
 pub(crate) mod parallel_eval;
 pub mod random_search;
+pub mod simulated_annealing;
 pub mod spea2;
 
 pub use differential_evolution::*;
@@ -17,4 +18,5 @@ pub use nsga2::*;
 pub use nsga3::*;
 pub use paes::*;
 pub use random_search::*;
+pub use simulated_annealing::*;
 pub use spea2::*;