feat(algorithms): add Tlbo (Teaching-Learning-Based Optimization)

Rao 2011 TLBO: parameter-free single-objective optimizer for Vec<f64>.
The selling point — uniquely among the metaheuristics we ship — is that
it has NO algorithm-specific hyperparameters: no F, CR, w, c1, c2, σ,
mutation rate, etc. Just population_size and generations.

Each generation has two phases:
- **Teacher phase**: identify the best individual (the 'teacher'). For
  every learner, compute a 'mean' learner and try replacing it with a
  candidate moved toward the teacher by a random fraction, scaled by
  the gap between teacher and (TF · mean), where TF ∈ {1, 2}.
- **Learner phase**: each learner picks a random partner and tries
  moving toward the better one of the pair. Only successful moves are
  kept.

Single-objective only, Vec<f64> only, bounds enforced via clamping.
Tests cover Sphere1D convergence, deterministic reruns, and panic on
multi-objective.

src/prelude.rs

@@ -30,6 +30,6 @@ pub use crate::algorithms::{
     ParticleSwarmConfig, RandomSearch, RandomSearchConfig, Rvea, RveaConfig,
     SimulatedAnnealing,
     SimulatedAnnealingConfig, SmsEmoa, SmsEmoaConfig, Spea2, Spea2Config, TabuSearch,
-    TabuSearchConfig, Umda,
+    TabuSearchConfig, Tlbo, TlboConfig, Umda,
     UmdaConfig,
 };