NSGA-III's value over NSGA-II shows up at 3+ objectives, where
crowding distance loses its diversity signal. Adds a third comparison
section to `examples/compare.rs`:
DTLZ2 (3-objective, 12-D, the textbook benchmark for many-objective
algorithms): unit-sphere-octant Pareto front. Compares RandomSearch,
NSGA-II, SPEA2, and NSGA-III on:
- mean distance from front points to the unit sphere
(closed-form: |1 - sqrt(f1² + f2² + f3²)|),
- spacing,
- front size,
- wall-clock ms.
NSGA-III config: H=12 reference divisions (91 reference points,
matching the canonical setup from Deb & Jain 2014).
Also wires NSGA-III into the existing ZDT1 (2-objective) section even
though it's not its sweet spot — useful as a regression check that the
algorithm at least keeps up with NSGA-II on bi-objective problems.
swaits
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