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>

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 |
-| MOEA/D    | 125.2413 ± 2.1647   | 0.0198 ± 0.0043 | 92    | 27  |
-| NSGA-II   | 123.1826 ± 1.5829   | **0.0092 ± 0.0020** | 98 | 40  |
-| AGE-MOEA  | 119.5132 ± 1.2732   | 0.0136 ± 0.0023 | 90    | 171 |
+| **IBEA**  | **126.2072 ± 1.2280** | 0.0164 ± 0.0036 | 48  | 126 |
+| MOEA/D    | 125.2413 ± 2.1647   | 0.0198 ± 0.0043 | 92    | 26  |
+| NSGA-II   | 123.1826 ± 1.5829   | **0.0092 ± 0.0020** | 98 | 39  |
+| 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 {