feat(algorithms): add RandomSearch baseline optimizer

The reference baseline and the spec's recommended starting example. Per
iteration it asks the initializer for `batch_size` decisions, evaluates
each, and accumulates them. At the end it returns the full population
plus the Pareto front and (if single-objective) the best feasible
candidate. `generations` equals `iterations`; `evaluations` equals
`iterations * batch_size` (spec §12.1).

Includes a tiny single-objective sphere test problem under
`tests_support` that later algorithm tests will reuse.

src/lib.rs

@@ -2,9 +2,13 @@
 //! many-objective optimization. See `docs/heuropt_tech_design_spec.md` for the
 //! full design.
 
+pub mod algorithms;
 pub mod core;
 pub mod operators;
 pub mod pareto;
 pub mod prelude;
 pub mod selection;
 pub mod traits;
+
+#[cfg(test)]
+pub(crate) mod tests_support;