`ParetoArchive::insert` calls `pareto_compare` twice per existing
member (once per pass), and each call re-allocates two Vec<f64>s
via `as_minimization` — 4N allocations per insert. Cache the
candidate's oriented + feasibility/violation once, build each
member's oriented vector once for the call, then inline the
dominance test against those cached arrays.
Used by PESA-II (per offspring per generation), PAES (per child),
ε-MOEA, and any user code working through the archive directly.
Wall-clock (compare harness, 10-seed mean):
- PESA-II / DTLZ2: 498 → 426 ms (-14 %)
- PESA-II / ZDT1: 87 → 75 ms (-14 %)
Smaller wins on PAES / MOPSO / IBEA / HypE / ε-MOEA where the
archive isn't the dominant per-generation cost.
Bit-identical via the compare harness.
The Deb fast non-dominated sort calls `pareto_compare` twice for
every (i, j) pair, and each `pareto_compare` call invokes
`ObjectiveSpace::as_minimization` twice — so for an N-point
population that's 4·N·(N-1) fresh `Vec<f64>` allocations per sort.
At N=100 with thousands of generations across the compare harness,
this dominated the per-generation cost of every Pareto-based MOEA.
Cache `as_minimization`/feasibility/violation once per individual
up front, then inline the dominance test against those cached
arrays. The output (per-pair dominance outcome and the per-i
`dominates` lists) is bit-identical to `pareto_compare`.
gungraun (instructions):
- non_dominated_sort_2d n=50: 852 317 → 198 574 (-77 %, 4.3×)
- non_dominated_sort_2d n=200: 13 513 271 → 2 601 813 (-81 %, 5.2×)
Wall-clock (compare harness, 10-seed mean):
- NSGA-II / ZDT1: 268 → 65 ms (4.1×)
- NSGA-II / ZDT3: 267 → 65 ms (4.1×)
- NSGA-II / DTLZ2: 344 → 106 ms (3.2×)
- NSGA-II / Rastrigin: 260 → 71 ms (3.7×)
- NSGA-III / DTLZ2: 318 → 122 ms (2.6×)
- NSGA-III / DTLZ1: 303 → 122 ms (2.5×)
- SMS-EMOA / DTLZ2: 1413 → 1369 ms (small additional win on top of HV)
- AGE-MOEA / DTLZ1: 430 → 229 ms (1.9×, on top of the AGE-MOEA caching)
- HypE / DTLZ2: 80 → 44 ms (1.8×)
The standard structured weight/reference vector generator for
many-objective MOEAs (NSGA-III, MOEA/D). Generates (H+M-1 choose M-1)
points uniformly distributed on the unit simplex by enumerating all
integer compositions of `divisions` into `num_objectives` parts and
dividing each by `divisions`.
Lives in src/pareto/reference_points.rs. Re-exported from the prelude
as `das_dennis`.
Tests cover: M=2/H=4 → 5 points along the diagonal; M=3/H=12 → 91
points (the canonical NSGA-III 3-objective ref set); each generated
point has exactly M components summing to 1 within float tolerance.
- pareto/crowding.rs: rewrite the inner loop to iterate per-objective
via index_axis-style indexing on `oriented` rather than naming an
unused loop variable `k`.
- operators/{binary,permutation}.rs tests: pass parents via
`std::slice::from_ref` instead of `&[parent.clone()]` to avoid the
cloned_ref_to_slice_refs lint.
Pure cleanup — no behavior change, all 83 unit tests + 2 doctests still
pass.
A concrete archive (not a trait — spec §13). On insert it discards the
new candidate if any existing member dominates it, then removes existing
members the new candidate dominates. `truncate` does simple
tail-truncation in v1; the doc note flags that crowding-aware
truncation is a future improvement.
Computes per-point crowding distance over a single Pareto front (spec
§9.6):
- Returns Vec<f64> with the same length as the front index slice.
- Empty front → empty Vec.
- Front of length ≤ 2 → all f64::INFINITY.
- Boundary points along each objective receive INFINITY.
- Interior points get sum of normalized neighbor gaps; if max == min for
an objective the contribution is zero.
- Operates on minimization-oriented objective values.
Deb's fast non-dominated sort: returns Vec<Vec<usize>> of front indices
into the input population, with fronts[0] being the non-dominated set.
O(N²·M) is acceptable for v1 (spec §9.5).
Tests cover: small known population produces expected fronts; equal
candidates land on the same front; an empty population yields no
fronts.
`pareto_front` returns all candidates not dominated by any other in
input order (O(N²·M), acceptable for v1 per spec §9.3).
`best_candidate` is the single-objective "best" finder: returns None
unless there is exactly one objective; ignores infeasibles; returns None
if every candidate is infeasible (spec §9.4).
Both re-exported from the prelude.
swaits