docs: correct disconnected-front and sequencing guidance from compare results

The `compare` harness contradicts two recommendations in the decision
trees:

- "Disconnected or non-convex front -> AGE-MOEA, KnEA, IBEA" had it
  backwards. Added KnEA to the ZDT3 table (the disconnected-front
  benchmark) so the claim is actually exercised: AGE-MOEA and KnEA
  finish *last and second-last*; IBEA wins, MOEA/D and NSGA-II follow.
  The trees now split "disconnected" from "non-convex contiguous",
  lead disconnected with IBEA, and note the geometry-aware methods
  trail when the front is in pieces.
- The book filed Simulated Annealing on permutations as a "one-decision
  baseline" and led the JSS row with GA. On the harness SA *wins* the
  FT06 job-shop table and ties for the TSP optimum; SA/Tabu edge out
  the GA. Reframed SA/Tabu as strong sequencing methods.

Also regenerated examples/compare-results.md for the new ZDT3 row.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

README.md

@@ -412,10 +412,13 @@ START
      │    │         → GA with BitFlipMutation +
      │    │           a bit-string crossover
      │    │
-     │    ├─ Decision is Vec<usize> (permutation, e.g., TSP)
+     │    ├─ Decision is Vec<usize> (permutation: TSP, JSS, …)
-     │    │     → Ant Colony (with a distance matrix)
+     │    │     → Ant Colony (TSP, with a distance matrix)
-     │    │     → Tabu Search (with your own neighbor function)
+     │    │     → Simulated Annealing / Tabu Search (strong on
-     │    │     → Simulated Annealing with SwapMutation
+     │    │        sequencing — they win the harness TSP and JSS
+     │    │        tables — you supply the neighbour move)
+     │    │     → GA + permutation toolkit (ERX for TSP-shaped
+     │    │        instances)
      │    │
      │    └─ Custom decision type (a struct, a tree, …)
      │          → Simulated Annealing or Hill Climber
@@ -448,10 +451,14 @@ START
      │    ├─ Want decomposition / weight-vector style
      │    │     → MOEA/D   (very fast per generation, scales well)
      │    │
-     │    ├─ Disconnected or non-convex front
+     │    ├─ Disconnected front (separate arcs, e.g. ZDT3)
+     │    │     → IBEA     (wins ZDT3 hypervolume on the harness;
+     │    │                 MOEA/D and NSGA-II follow. Geometry-aware
+     │    │                 methods trail when the front is in pieces)
+     │    │
+     │    ├─ Non-convex but *contiguous* front
      │    │     → AGE-MOEA (estimates front geometry adaptively)
      │    │     → KnEA     (favors knee points)
-     │    │     → IBEA
      │    │
      │    ├─ Want region-based diversity
      │    │     → PESA-II  (grid hyperboxes drive selection)

docs/book/src/choosing-an-algorithm.md

@@ -104,9 +104,9 @@ optimizer or with the problem).
 | `Vec<bool>` | [GA][GeneticAlgorithm] + [`BitFlipMutation`] | When bit interactions matter. |
 | `Vec<usize>` (permutation) | [Ant Colony][AntColonyTsp] | TSP-style with a distance matrix. |
 | `Vec<usize>` (permutation) | [GA][GeneticAlgorithm] + [`ShuffledPermutation`] + [`OrderCrossover`] + [`InversionMutation`] | Generic permutation GA; use [`EdgeRecombinationCrossover`] for TSP-shaped instances. |
-| `Vec<usize>` (JSS multiset) | [GA][GeneticAlgorithm] + [`ShuffledMultisetPermutation`] + local POX + [`InversionMutation`] | Operation-string encoding; see [Optimize a permutation](./cookbook/permutation.md). |
+| `Vec<usize>` (JSS multiset) | [Simulated Annealing][SimulatedAnnealing] / [Tabu Search][TabuSearch] with [`InsertionMutation`], or [GA][GeneticAlgorithm] + [`ShuffledMultisetPermutation`] + local POX | Operation-string encoding. On the FT06 harness the local-search pair edges out the GA — see [Optimize a permutation](./cookbook/permutation.md). |
-| `Vec<usize>` (permutation) | [Simulated Annealing][SimulatedAnnealing] + [`SwapMutation`] | One-decision baseline. |
+| `Vec<usize>` (permutation) | [Simulated Annealing][SimulatedAnnealing] + [`InversionMutation`] | Strong on sequencing, not just a baseline — wins the harness's FT06 job-shop table and ties for the TSP optimum. |
-| `Vec<usize>` or custom | [Tabu Search][TabuSearch] | You supply the neighbor function. |
+| `Vec<usize>` or custom | [Tabu Search][TabuSearch] | You supply the neighbor function; consistently near the top on the TSP and JSS tables. |
 | Custom struct | [Simulated Annealing][SimulatedAnnealing] / [Hill Climber][HillClimber] | With your own `Variation` impl. |
 
 heuropt's permutation operator toolkit covers four crossovers
@@ -161,14 +161,24 @@ objectives.
 
 ### Disconnected or non-convex front
 
+A *disconnected* front (separate arcs, like ZDT3) and a *non-convex but
+contiguous* front are different problems — don't conflate them.
+
+For a **disconnected** front, [IBEA][Ibea] is the clear pick: on the
+harness it wins ZDT3 — the disconnected-front benchmark — outright on
+hypervolume, with [MOEA/D][Moead] and [NSGA-II][Nsga2] close behind.
+Counter-intuitively the geometry-aware methods below *trail* here:
+estimating a single front geometry or chasing knee points doesn't help
+when the front is in pieces (on ZDT3, AGE-MOEA and KnEA finish last).
+
+For a **non-convex but contiguous** front:
+
 [AGE-MOEA][AgeMoea] estimates the front geometry adaptively (the L_p
 parameter `p` is fit from data each generation).
 
 [KnEA][Knea] favors knee points — the regions of the front where small
 gains in one objective cost large losses in another.
 
-[IBEA][Ibea] also handles disconnected fronts well.
-
 ### Region-based diversity
 
 [PESA-II][PesaII] uses grid hyperboxes to drive selection — divide the

examples/compare-results.md

@@ -60,10 +60,17 @@ dominated gaps between them.
 
 | algorithm | hypervolume ↑       | spacing ↓       | front | ms  |
 |---|---|---|---|---|
-| **IBEA**  | **126.2072 ± 1.2280** | 0.0164 ± 0.0036 | 48  | 131 |
+| **IBEA**  | **126.2072 ± 1.2280** | 0.0164 ± 0.0036 | 48  | 126 |
-| MOEA/D    | 125.2413 ± 2.1647   | 0.0198 ± 0.0043 | 92    | 27  |
+| MOEA/D    | 125.2413 ± 2.1647   | 0.0198 ± 0.0043 | 92    | 26  |
-| NSGA-II   | 123.1826 ± 1.5829   | **0.0092 ± 0.0020** | 98 | 40  |
+| NSGA-II   | 123.1826 ± 1.5829   | **0.0092 ± 0.0020** | 98 | 39  |
-| AGE-MOEA  | 119.5132 ± 1.2732   | 0.0136 ± 0.0023 | 90    | 171 |
+| AGE-MOEA  | 119.5132 ± 1.2732   | 0.0136 ± 0.0023 | 90    | 170 |
+| KnEA      | 117.2180 ± 0.7027   | 0.0147 ± 0.0049 | 79    | 32  |
+
+The **geometry-aware methods finish last** on the disconnected front:
+AGE-MOEA and KnEA both trail the dominance- and decomposition-based
+methods. Estimating a single front geometry — or chasing knee points —
+doesn't help when the front is in pieces; IBEA's indicator-based
+selection wins here.
 
 ## DTLZ2 (3-obj, dim=12, 30000 evals/run × 10 seeds)
 

examples/compare.rs

@@ -1574,6 +1574,29 @@ fn zdt3_age_moea(seed: u64) -> MoRun {
     }
 }
 
+fn zdt3_knea(seed: u64) -> MoRun {
+    let problem = zdt3_problem();
+    let bounds = vec![(0.0, 1.0); ZDT3_DIM];
+    let initializer = RealBounds::new(bounds.clone());
+    let variation = CompositeVariation {
+        crossover: SimulatedBinaryCrossover::new(bounds.clone(), 15.0, 0.5),
+        mutation: PolynomialMutation::new(bounds, 20.0, 1.0 / ZDT3_DIM as f64),
+    };
+    let pop = 100;
+    let config = KneaConfig {
+        population_size: pop,
+        generations: ZDT3_BUDGET / pop,
+        seed,
+    };
+    let mut opt = Knea::new(config, initializer, variation);
+    let t0 = Instant::now();
+    let result = opt.run(&problem);
+    MoRun {
+        front: result.pareto_front,
+        wall_ms: t0.elapsed().as_millis(),
+    }
+}
+
 // -----------------------------------------------------------------------------
 // DTLZ1 runners (curated many-obj subset)
 // -----------------------------------------------------------------------------
@@ -1998,6 +2021,7 @@ fn run_zdt3_comparison() {
         ("MOEA/D", zdt3_moead),
         ("IBEA", zdt3_ibea),
         ("AGE-MOEA", zdt3_age_moea),
+        ("KnEA", zdt3_knea),
     ];
     let mut rows: Vec<(f64, Vec<String>)> = Vec::new();
     for (name, runner) in runners {