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/prelude.rs

@@ -24,5 +24,6 @@ pub use crate::operators::{
 pub use crate::algorithms::{
     DifferentialEvolution, DifferentialEvolutionConfig, HillClimber, HillClimberConfig,
     Moead, MoeadConfig, Nsga2, Nsga2Config, Nsga3, Nsga3Config, Paes, PaesConfig,
-    RandomSearch, RandomSearchConfig, Spea2, Spea2Config,
+    RandomSearch, RandomSearchConfig, SimulatedAnnealing, SimulatedAnnealingConfig,
+    Spea2, Spea2Config,
 };