docs: 0.8.0 release polish — README, guide, changelog

Companion to the feat(async) commit. Brings every cross-referencing
doc up to v0.8 currency, replaces marketing-flavored copy with plain
prose, and replaces toy benchmark problems with relatable ones that
include actual run output and interpretive narrative.

- README: collapses the four-bullet "Read the user guide / API
  reference / Tested with N tests / Hot paths optimized" list into
  a single Docs links line.
- README: replaces the Schaffer-N1 toy problem with a PickACar
  multi-objective design problem — three decision variables
  (displacement, weight, drag), four objectives (price, 0-60,
  fuel, noise), and *nonlinear* cost relationships so the Pareto
  front is a real surface, not a 1D sweep. Includes actual NSGA-III
  run output (representative slice across the 100-car front) and
  a narrative explaining what each row tells you and why hand-
  picking would miss the interesting tradeoffs.
- README: removes rustdoc-style hidden `#` setup lines from code
  blocks. The README is rendered as plain markdown on GitHub /
  crates.io, where those lines are visible garbage instead of
  hidden setup. Code blocks are now self-contained.
- Guide quickstart (getting-started.md): replaces Sphere ( Σ x² )
  with a least-squares LineFit example. Same shape (single-
  objective continuous), but recognizable framing. Includes
  actual CMA-ES output, residual table, and narrative comparing
  the answer to standard regression.
- Algorithm count audit: stale "35 algorithms" claim corrected to
  the actual 33 across README, src/lib.rs, introduction.md, and
  the comparison.md table cell.
- Async feature flag listed in the optional-features sections of
  README, src/lib.rs, getting-started.md.
- introduction.md, choosing-an-algorithm.md, comparison.md,
  stability.md, migration.md, cookbook/parallel.md,
  cookbook/custom-optimizer.md: cross-references updated to
  describe full async coverage and link the new cookbook recipe.
- stability.md: removes the speculative "Observer / Snapshot /
  Checkpoint planned" bullet (those didn't ship); documents the
  AsyncProblem / AsyncPartialProblem trait stability.
- migration.md: new "To 0.8" section with paths from 0.5.x and 0.7.x.
- CHANGELOG: 0.8.0 entry capturing the async feature plus the
  documentation / governance / CI catch-up.
- SECURITY.md: supported versions table reflects 0.8.x.

docs/book/src/comparison.md

@@ -15,11 +15,11 @@ The columns:
 
 | Library | Lang | Algorithms | Multi-obj | Surrogates | Determinism | Async |
 |---|---|---|---|---|---|---|
-| **heuropt 0.5** | Rust | 35 | ✅ NSGA-II/III, SPEA2, IBEA, MOEA/D, MOPSO, SMS-EMOA, HypE, AGE-MOEA, GrEA, KnEA, RVEA, PESA-II, ε-MOEA, PAES | ✅ BO, TPE, Hyperband | ✅ bit-identical seeded | ⏳ planned |
+| **heuropt 0.8** | Rust | 33 | ✅ NSGA-II/III, SPEA2, IBEA, MOEA/D, MOPSO, SMS-EMOA, HypE, AGE-MOEA, GrEA, KnEA, RVEA, PESA-II, ε-MOEA, PAES | ✅ BO, TPE, Hyperband | ✅ bit-identical seeded | ✅ `AsyncProblem` + `run_async` on every algorithm |
 | pymoo | Python | ~25 | ✅ extensive | partial (BO via plug-ins) | ✅ | ❌ |
 | DEAP | Python | flexible toolbox | ✅ | ❌ | ✅ | ❌ |
 | hyperopt | Python | TPE-focused | ❌ | ✅ TPE | partial | partial |
-| optuna | Python | TPE / CMA-ES / NSGA-II | ✅ | ✅ TPE, BoTorch via plug-in | ✅ | ✅ |
+| optuna | Python | TPE / CMA-ES / NSGA-II | ✅ | ✅ TPE, BoTorch via plug-in | ✅ | partial (study-level, not eval-level) |
 | MOEA Framework | Java | ~40 | ✅ very extensive | ❌ | ✅ | ❌ |
 | metaheuristics-rs | Rust | ~10 | partial | ❌ | ✅ | ❌ |
 | argmin | Rust | line-search / quasi-Newton | ❌ | ❌ | ✅ | ❌ |
@@ -38,12 +38,13 @@ The columns:
   written for clarity, no trait-object plumbing, no GATs in user-
   facing APIs. Reading `RandomSearch` should be enough to write a
   new optimizer.
+- You have **IO-bound evaluations** — calling an HTTP service, an
+  RPC, or a subprocess — and want first-class `async fn evaluate`
+  support. heuropt is the only mainstream optimization library that
+  ships this (see [Async evaluation](./cookbook/async.md)).
 
 ## When *not* to pick heuropt
 
-- You need **first-class async / await** for evaluations that talk to
-  HTTP services or spawn subprocesses. heuropt is sync; that's on
-  the roadmap but not shipping yet.
 - You need **gradient-based** optimization. Use `argmin` (Rust) or
   `scipy.optimize` (Python) — heuropt is gradient-free by design.
 - You need **GPU-accelerated** evaluations. heuropt's `evaluate`