refactor(examples): split multi-concept examples into focused single-topic files

- Split pruning_and_callbacks into pruning and early_stopping
- Split advanced_features into async_parallel, journal_storage, ask_and_tell, multi_objective
- Each example now requires only its own feature flag
- Trim sampler_comparison winner logic and verbose header
- Update CI workflow and README to match new example names

examples/multi_objective.rs

@@ -0,0 +1,49 @@
+//! Multi-objective optimization — optimize competing objectives simultaneously.
+//!
+//! `MultiObjectiveStudy` returns the Pareto front: the set of solutions where
+//! no objective can be improved without worsening another.
+//!
+//! Run with: `cargo run --example multi_objective`
+
+use optimizer::multi_objective::MultiObjectiveStudy;
+use optimizer::prelude::*;
+
+fn main() -> optimizer::Result<()> {
+    let study = MultiObjectiveStudy::new(vec![Direction::Minimize, Direction::Minimize]);
+
+    let x = FloatParam::new(0.0, 1.0).name("x");
+
+    // Classic bi-objective: f1(x) = x², f2(x) = (x-1)²
+    // The Pareto front is the curve where improving f1 worsens f2.
+    study.optimize(50, |trial| {
+        let xv = x.suggest(trial)?;
+        let f1 = xv * xv;
+        let f2 = (xv - 1.0) * (xv - 1.0);
+        Ok::<_, optimizer::Error>(vec![f1, f2])
+    })?;
+
+    let front = study.pareto_front();
+    println!(
+        "Ran {} trials, Pareto front has {} solutions:",
+        study.n_trials(),
+        front.len(),
+    );
+
+    let mut sorted = front.clone();
+    sorted.sort_by(|a, b| a.values[0].partial_cmp(&b.values[0]).unwrap());
+
+    for (i, trial) in sorted.iter().take(5).enumerate() {
+        println!(
+            "  {}: x={:.3}, f1={:.4}, f2={:.4}",
+            i + 1,
+            trial.get(&x).unwrap(),
+            trial.values[0],
+            trial.values[1],
+        );
+    }
+    if sorted.len() > 5 {
+        println!("  ... and {} more", sorted.len() - 5);
+    }
+
+    Ok(())
+}