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

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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>
This commit is contained in:
swaits
2026-05-14 08:43:55 -06:00
co-authored by Claude Opus 4.7
parent 7004a572c8
commit a9d72b94f4
4 changed files with 63 additions and 15 deletions
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examples/compare-results.md
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@@ -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)