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/knea.rs

@@ -149,6 +149,77 @@ where
     }
 }
 
+#[cfg(feature = "async")]
+impl<I, V> Knea<I, V> {
+    /// Async version of [`Optimizer::run`] — drives evaluations through
+    /// the user-chosen async runtime. Available only with the `async`
+    /// feature.
+    ///
+    /// `concurrency` bounds in-flight evaluations per batch.
+    pub async fn run_async<P>(
+        &mut self,
+        problem: &P,
+        concurrency: usize,
+    ) -> OptimizationResult<P::Decision>
+    where
+        P: crate::core::async_problem::AsyncProblem,
+        I: Initializer<P::Decision>,
+        V: Variation<P::Decision>,
+    {
+        use crate::algorithms::parallel_eval_async::evaluate_batch_async;
+
+        assert!(
+            self.config.population_size > 0,
+            "Knea population_size must be > 0"
+        );
+        let n = self.config.population_size;
+        let objectives = problem.objectives();
+        let mut rng = rng_from_seed(self.config.seed);
+
+        let initial_decisions = self.initializer.initialize(n, &mut rng);
+        let mut population: Vec<Candidate<P::Decision>> =
+            evaluate_batch_async(problem, initial_decisions, concurrency).await;
+        let mut evaluations = population.len();
+
+        for _ in 0..self.config.generations {
+            let mut offspring_decisions: Vec<P::Decision> = Vec::with_capacity(n);
+            while offspring_decisions.len() < n {
+                let p1 = rng.random_range(0..population.len());
+                let p2 = rng.random_range(0..population.len());
+                let parents = vec![
+                    population[p1].decision.clone(),
+                    population[p2].decision.clone(),
+                ];
+                let children = self.variation.vary(&parents, &mut rng);
+                assert!(!children.is_empty(), "Knea variation returned no children");
+                for child in children {
+                    if offspring_decisions.len() >= n {
+                        break;
+                    }
+                    offspring_decisions.push(child);
+                }
+            }
+            let offspring = evaluate_batch_async(problem, offspring_decisions, concurrency).await;
+            evaluations += offspring.len();
+
+            let mut combined: Vec<Candidate<P::Decision>> = Vec::with_capacity(2 * n);
+            combined.extend(population);
+            combined.extend(offspring);
+            population = environmental_selection(combined, &objectives, n);
+        }
+
+        let front = pareto_front(&population, &objectives);
+        let best = best_candidate(&population, &objectives);
+        OptimizationResult::new(
+            Population::new(population),
+            front,
+            best,
+            evaluations,
+            self.config.generations,
+        )
+    }
+}
+
 fn environmental_selection<D: Clone>(
     combined: Vec<Candidate<D>>,
     objectives: &ObjectiveSpace,