# `ExVrp.IteratedLocalSearch`
[🔗](https://github.com/sephianl/ex_vrp/blob/v0.4.2/lib/ex_vrp/iterated_local_search.ex#L1)

Iterated Local Search with Late Acceptance Hill-Climbing.

This is a direct port of PyVRP's IteratedLocalSearch. The algorithm:
1. Starts with an initial solution
2. Each iteration: applies local search to generate a candidate
3. Accepts the candidate using Late Acceptance Hill-Climbing (LAHC)
4. Restarts from best solution after N iterations without improvement
5. Continues until stopping criterion is met

Late Acceptance Hill-Climbing (Burke & Bykov, 2017) accepts moves based on
comparison with historical solutions, not just the current solution. This
allows temporary worsening moves to escape local optima.

# `stop_fn`

```elixir
@type stop_fn() :: (non_neg_integer() -> boolean())
```

# `run`

```elixir
@spec run(
  reference(),
  ExVrp.PenaltyManager.t(),
  reference(),
  reference(),
  stop_fn(),
  ExVrp.IteratedLocalSearch.Params.t(),
  keyword()
) :: ExVrp.IteratedLocalSearch.Result.t()
```

Runs the Iterated Local Search algorithm.

## Parameters

- `problem_data` - Reference to the problem data
- `penalty_manager` - PenaltyManager for dynamic penalty adjustment
- `local_search` - Persistent LocalSearch resource from Native.create_local_search/1
- `initial_solution` - Starting solution reference
- `stop_fn` - Function that takes best_cost and returns true to stop
- `params` - ILS parameters (optional)

## Returns

A Result struct containing the best solution found and statistics.

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*Consult [api-reference.md](api-reference.md) for complete listing*