Files
heuropt/src/pareto/crowding.rs
T

142 lines
4.3 KiB
Rust

//! Crowding distance for diversity preservation in NSGA-II-style algorithms.
use crate::core::candidate::Candidate;
use crate::core::objective::ObjectiveSpace;
/// Compute crowding distance for the given front (a slice of indices into the
/// population).
///
/// Returns a `Vec<f64>` aligned with `front` (so `result[i]` is the crowding
/// distance of `population[front[i]]`). Boundary points receive
/// `f64::INFINITY`. If the front has 0 entries an empty vector is returned;
/// 1 or 2 entries return all `f64::INFINITY`. All comparisons happen on
/// minimization-oriented objective values (spec §9.6).
pub fn crowding_distance<D>(
population: &[Candidate<D>],
front: &[usize],
objectives: &ObjectiveSpace,
) -> Vec<f64> {
let n = front.len();
if n == 0 {
return Vec::new();
}
if n <= 2 {
return vec![f64::INFINITY; n];
}
let m = objectives.len();
let mut distance = vec![0.0_f64; n];
// Cache minimization-oriented objective values for each front member.
let oriented: Vec<Vec<f64>> = front
.iter()
.map(|&idx| objectives.as_minimization(&population[idx].evaluation.objectives))
.collect();
#[allow(clippy::needless_range_loop)] // `k` indexes into nested vectors below.
for k in 0..m {
// Sort indices into `front` by objective k.
let mut order: Vec<usize> = (0..n).collect();
order.sort_by(|&a, &b| {
oriented[a][k]
.partial_cmp(&oriented[b][k])
.unwrap_or(std::cmp::Ordering::Equal)
});
distance[order[0]] = f64::INFINITY;
distance[order[n - 1]] = f64::INFINITY;
let f_min = oriented[order[0]][k];
let f_max = oriented[order[n - 1]][k];
let span = f_max - f_min;
if span == 0.0 {
continue;
}
for i in 1..n - 1 {
if distance[order[i]] == f64::INFINITY {
continue;
}
let prev = oriented[order[i - 1]][k];
let next = oriented[order[i + 1]][k];
distance[order[i]] += (next - prev) / span;
}
}
distance
}
#[cfg(test)]
mod tests {
use super::*;
use crate::core::evaluation::Evaluation;
use crate::core::objective::Objective;
fn cand(obj: Vec<f64>) -> Candidate<()> {
Candidate::new((), Evaluation::new(obj))
}
fn space_min2() -> ObjectiveSpace {
ObjectiveSpace::new(vec![Objective::minimize("f1"), Objective::minimize("f2")])
}
#[test]
fn empty_front_returns_empty_vec() {
let s = space_min2();
let pop: [Candidate<()>; 0] = [];
let d = crowding_distance(&pop, &[], &s);
assert!(d.is_empty());
}
#[test]
fn single_point_is_infinity() {
let s = space_min2();
let pop = [cand(vec![1.0, 2.0])];
let d = crowding_distance(&pop, &[0], &s);
assert_eq!(d, vec![f64::INFINITY]);
}
#[test]
fn two_points_both_infinity() {
let s = space_min2();
let pop = [cand(vec![1.0, 2.0]), cand(vec![2.0, 1.0])];
let d = crowding_distance(&pop, &[0, 1], &s);
assert_eq!(d, vec![f64::INFINITY, f64::INFINITY]);
}
#[test]
fn boundary_infinity_interior_finite() {
let s = space_min2();
// Three non-dominated points along a Pareto-like trade-off:
// 0: (0, 4)
// 1: (2, 2) ← interior on both axes
// 2: (4, 0)
let pop = [
cand(vec![0.0, 4.0]),
cand(vec![2.0, 2.0]),
cand(vec![4.0, 0.0]),
];
let d = crowding_distance(&pop, &[0, 1, 2], &s);
assert!(d[0].is_infinite());
assert!(d[2].is_infinite());
assert!(d[1].is_finite());
assert!(d[1] > 0.0);
}
#[test]
fn equal_objective_axis_does_not_panic() {
// All points share the same f2 value; the f2 axis contributes zero,
// and the f1 axis still produces sensible boundary infinities.
let s = space_min2();
let pop = [
cand(vec![0.0, 1.0]),
cand(vec![1.0, 1.0]),
cand(vec![2.0, 1.0]),
];
let d = crowding_distance(&pop, &[0, 1, 2], &s);
assert!(d[0].is_infinite());
assert!(d[2].is_infinite());
assert!(d[1].is_finite());
}
}