feat(async): add run_async to every algorithm in the catalog

Async coverage was incomplete in 0.7 (only RandomSearch and
DifferentialEvolution had run_async). 0.8 closes the gap: every one
of the 33 algorithms now exposes
run_async(&problem, concurrency).await, gated on the async feature.

- Population-based algorithms fan out per-generation evaluations
  through evaluate_batch_async with concurrency-bounded
  FuturesOrdered chunks.
- Steady-state algorithms (HillClimber, SimulatedAnnealing,
  OnePlusOneEs, Paes, NelderMead) await each step sequentially;
  they accept the concurrency parameter for API uniformity.
- TabuSearch fans out the K-neighbor batch each step.
- Surrogate algorithms (BayesianOpt, Tpe) batch the initial design
  and await per-iteration acquisitions sequentially so the surrogate
  can update between picks.
- Hyperband uses a new AsyncPartialProblem trait (mirroring
  PartialProblem for multi-fidelity workloads) and a parallel
  evaluate_batch_at_budget_async helper; each Successive-Halving
  rung fans out its budgeted evaluations.

All paths preserve seeded determinism: RNG draws happen on the main
task in the same order as the sync path, and only the evaluations
are concurrent.

Adds a dedicated cookbook recipe at docs/book/src/cookbook/async.md
with a worked example (DifferentialEvolution under tokio) and
guidance on picking concurrency. Cross-references in SUMMARY.md
and cookbook.md are updated to surface the new recipe.

The follow-up docs commit reconciles the rest of the user guide
and README to describe the new feature; this commit is the bare
async surface.

src/algorithms/hyperband.rs

@@ -206,6 +206,106 @@ where
     }
 }
 
+#[cfg(feature = "async")]
+impl<I, D> Hyperband<I, D>
+where
+    D: Clone,
+    I: Initializer<D>,
+{
+    /// Async version of [`Hyperband::run`] — evaluates each
+    /// Successive-Halving rung's configurations concurrently through the
+    /// caller's async runtime. Available only with the `async` feature.
+    ///
+    /// `concurrency` bounds in-flight evaluations per rung.
+    pub async fn run_async<P>(&mut self, problem: &P, concurrency: usize) -> OptimizationResult<D>
+    where
+        P: crate::core::async_problem::AsyncPartialProblem<Decision = D>,
+        D: Send + Sync,
+    {
+        use crate::algorithms::parallel_eval_async::evaluate_batch_at_budget_async;
+
+        assert!(
+            self.config.max_budget > 0.0,
+            "Hyperband max_budget must be > 0"
+        );
+        assert!(self.config.eta > 1.0, "Hyperband eta must be > 1");
+        assert!(
+            self.config.max_brackets >= 1,
+            "Hyperband max_brackets must be >= 1"
+        );
+        let objectives = problem.objectives();
+        assert!(
+            objectives.is_single_objective(),
+            "Hyperband requires exactly one objective",
+        );
+        let direction = objectives.objectives[0].direction;
+        let mut rng = rng_from_seed(self.config.seed);
+
+        let s_max = (self.config.max_budget.ln() / self.config.eta.ln()).floor() as i64;
+        let s_max = (s_max as usize).min(self.config.max_brackets);
+
+        let mut total_evaluations = 0usize;
+        let mut total_iterations = 0usize;
+        let mut best_seen: Option<Candidate<D>> = None;
+
+        for s in (0..=s_max).rev() {
+            let s_f = s as f64;
+            let n =
+                ((s_max as f64 + 1.0) / (s_f + 1.0) * self.config.eta.powf(s_f)).ceil() as usize;
+            let r = self.config.max_budget / self.config.eta.powf(s_f);
+
+            let mut configs: Vec<D> = self.initializer.initialize(n, &mut rng);
+            for i in 0..=s {
+                let n_i = (n as f64 / self.config.eta.powi(i as i32)).floor() as usize;
+                let r_i = r * self.config.eta.powi(i as i32);
+                if configs.is_empty() {
+                    break;
+                }
+                let evals: Vec<Evaluation> =
+                    evaluate_batch_at_budget_async(problem, &configs, r_i, concurrency).await;
+                total_evaluations += configs.len();
+
+                for (cfg, e) in configs.iter().zip(evals.iter()) {
+                    let beats = match &best_seen {
+                        None => true,
+                        Some(b) => better(e, &b.evaluation, direction),
+                    };
+                    if beats {
+                        best_seen = Some(Candidate::new(cfg.clone(), e.clone()));
+                    }
+                }
+                total_iterations += 1;
+
+                let next_size = (n_i / self.config.eta as usize).max(1);
+                if next_size >= configs.len() {
+                    continue;
+                }
+                let mut order: Vec<usize> = (0..configs.len()).collect();
+                order.sort_by(|&a, &b| compare(&evals[a], &evals[b], direction));
+                let keep: std::collections::HashSet<usize> =
+                    order.into_iter().take(next_size).collect();
+                let new_configs: Vec<D> = configs
+                    .into_iter()
+                    .enumerate()
+                    .filter_map(|(idx, c)| if keep.contains(&idx) { Some(c) } else { None })
+                    .collect();
+                configs = new_configs;
+            }
+        }
+
+        let best = best_seen.expect("at least one bracket ran");
+        let population = Population::new(vec![best.clone()]);
+        let front = vec![best.clone()];
+        OptimizationResult::new(
+            population,
+            front,
+            Some(best),
+            total_evaluations,
+            total_iterations,
+        )
+    }
+}
+
 fn compare(a: &Evaluation, b: &Evaluation, direction: Direction) -> std::cmp::Ordering {
     match (a.is_feasible(), b.is_feasible()) {
         (true, false) => std::cmp::Ordering::Less,