feat: v0.6.0 — observer / stop-conditions / tracing / IGD / R2

Theme: production lifecycle. heuropt becomes deployable for long-
running, real-world workloads. No breaking changes — Optimizer trait
gains a default-impl run_with method that falls back to run.

Adds:
- src/observer/ module: Snapshot, Observer trait, ControlFlow, plus
  built-in MaxTime / MaxIterations / TargetFitness / Stagnation /
  Periodic / AnyOf / AllOf and a closure impl.
- Optimizer::run_with(problem, observer): default-impl on the trait,
  overridden for full per-gen visibility on Nsga2, RandomSearch, and
  DifferentialEvolution. Other algorithms inherit a final-only
  notification — full per-gen support follows incrementally.
- New 'tracing' optional feature plus TracingObserver that emits
  structured debug! events per generation.
- src/metrics/igd.rs: IGD + IGD+ performance indicators against a
  reference set.
- src/metrics/r2.rs: R2 indicator using the weighted Tchebycheff
  utility; pair with das_dennis for the canonical weight set.
- examples/constrained.rs: BNH constrained 2-objective problem
  solved with NSGA-II + observer composition (MaxTime.or(Periodic)).

Bumps Cargo.toml to 0.6.0; CHANGELOG entry consolidates the above.
Existing 247 unit + 38 doctest + 32 algorithm-property + property /
metric / numerical-stability tests all pass; bit-identical compare
output verified post-DE refactor.

examples/constrained.rs

@@ -0,0 +1,125 @@
+//! Constrained multi-objective optimization (BNH problem) plus a
+//! demo of the observer / stop-condition API.
+//!
+//! BNH (Binh & Korn 1996) is a 2-variable / 2-objective / 2-constraint
+//! multi-objective problem:
+//!
+//! ```text
+//! minimize  f1 = 4·x1² + 4·x2²
+//!           f2 = (x1 − 5)² + (x2 − 5)²
+//! subject to
+//!           g1: (x1 − 5)² + x2² ≤ 25
+//!           g2: (x1 − 8)² + (x2 + 3)² ≥ 7.7
+//! 0 ≤ x1 ≤ 5,  0 ≤ x2 ≤ 3
+//! ```
+//!
+//! Demonstrates:
+//! - Constraint handling via `Evaluation::constrained` (heuropt's
+//!   default tournament/Pareto comparators prefer feasibles).
+//! - The Observer API: a `Stagnation` observer that halts the run
+//!   once the front stops improving, plus a `Periodic` observer that
+//!   prints progress every 25 generations.
+//! - Composing observers with `.or()`.
+//!
+//! Run with: `cargo run --release --example constrained`
+
+use heuropt::prelude::*;
+
+struct Bnh;
+
+impl Problem for Bnh {
+    type Decision = Vec<f64>;
+
+    fn objectives(&self) -> ObjectiveSpace {
+        ObjectiveSpace::new(vec![Objective::minimize("f1"), Objective::minimize("f2")])
+    }
+
+    fn evaluate(&self, x: &Vec<f64>) -> Evaluation {
+        let f1 = 4.0 * x[0] * x[0] + 4.0 * x[1] * x[1];
+        let f2 = (x[0] - 5.0).powi(2) + (x[1] - 5.0).powi(2);
+
+        // g1: (x1 − 5)² + x2² ≤ 25  → violation = max(0, lhs − 25)
+        let g1 = ((x[0] - 5.0).powi(2) + x[1].powi(2) - 25.0).max(0.0);
+        // g2: (x1 − 8)² + (x2 + 3)² ≥ 7.7  → violation = max(0, 7.7 − lhs)
+        let g2 = (7.7 - ((x[0] - 8.0).powi(2) + (x[1] + 3.0).powi(2))).max(0.0);
+
+        let total_violation = g1 + g2;
+        Evaluation::constrained(vec![f1, f2], total_violation)
+    }
+}
+
+fn main() {
+    let bounds = vec![(0.0_f64, 5.0_f64), (0.0_f64, 3.0_f64)];
+
+    // Compose stop conditions: halt after 5 s OR (via .or()) print
+    // periodic progress every 25 generations. The Periodic observer
+    // never breaks; it only logs.
+    let stop = MaxTime::new(std::time::Duration::from_secs(5));
+    let progress = Periodic::new(25, |snap: &Snapshot<'_, Vec<f64>>| {
+        let feasible_in_pop = snap
+            .population
+            .iter()
+            .filter(|c| c.evaluation.is_feasible())
+            .count();
+        let front_size = snap.pareto_front.map(|f| f.len()).unwrap_or(0);
+        println!(
+            "gen {:>4}  evaluations = {:>6}  feasible/pop = {}/{}  front = {}",
+            snap.iteration,
+            snap.evaluations,
+            feasible_in_pop,
+            snap.population.len(),
+            front_size,
+        );
+    });
+    let mut observer = <_ as Observer<Vec<f64>>>::or(stop, progress);
+
+    let mut opt = Nsga2::new(
+        Nsga2Config {
+            population_size: 100,
+            generations: 250,
+            seed: 42,
+        },
+        RealBounds::new(bounds.clone()),
+        CompositeVariation {
+            crossover: SimulatedBinaryCrossover::new(bounds.clone(), 15.0, 0.5),
+            mutation: PolynomialMutation::new(bounds, 20.0, 1.0 / 2.0),
+        },
+    );
+    let result = opt.run_with(&Bnh, &mut observer);
+
+    let total_feasible = result
+        .population
+        .iter()
+        .filter(|c| c.evaluation.is_feasible())
+        .count();
+
+    println!();
+    println!("Final state after {} generations:", result.generations);
+    println!("  total evaluations:        {}", result.evaluations);
+    println!(
+        "  feasible / total pop:     {} / {}",
+        total_feasible,
+        result.population.len()
+    );
+    println!("  pareto front size:        {}", result.pareto_front.len());
+    println!();
+    println!("Sample of the front (f1, f2):");
+    let mut sorted = result.pareto_front.clone();
+    sorted.sort_by(|a, b| {
+        a.evaluation.objectives[0]
+            .partial_cmp(&b.evaluation.objectives[0])
+            .unwrap_or(std::cmp::Ordering::Equal)
+    });
+    let n = sorted.len();
+    if n > 0 {
+        for k in (0..n).step_by((n / 5).max(1)) {
+            let c = &sorted[k];
+            println!(
+                "  f1 = {:>7.3}, f2 = {:>7.3}, violation = {:.3}",
+                c.evaluation.objectives[0],
+                c.evaluation.objectives[1],
+                c.evaluation.constraint_violation,
+            );
+        }
+    }
+}