Update scenario solver parameters

ddwp/static_solvers/scenario_solver.py

@@ -1,10 +1,9 @@
 import warnings
 
+from ddwp.VrpInstance import VrpInstance
 from pyvrp import (
     GeneticAlgorithm,
-    GeneticAlgorithmParams,
     PenaltyManager,
-    PenaltyParams,
     Population,
     PopulationParams,
     RandomNumberGenerator,
@@ -16,18 +15,16 @@
 from pyvrp.exceptions import EmptySolutionWarning
 from pyvrp.search import (
     Exchange10,
-    Exchange11,
     LocalSearch,
     NeighbourhoodParams,
+    RelocateStar,
     SwapRoutes,
     SwapStar,
     TwoOpt,
     compute_neighbours,
 )
 from pyvrp.stop import MaxRuntime
 
-from ddwp.VrpInstance import VrpInstance
-
 from .instance2data import instance2data
 
 warnings.filterwarnings("ignore", category=EmptySolutionWarning)
@@ -59,42 +56,31 @@ def scenario_solver(
     AssertionError
         If no feasible solution is found.
     """
-    gen_params = GeneticAlgorithmParams(repair_probability=0.5)
-    pen_params = PenaltyParams(
-        init_time_warp_penalty=14,
-        repair_booster=12,
-        num_registrations_between_penalty_updates=20,
-        penalty_increase=2,
-        penalty_decrease=0.34,
-        target_feasible=0.19,
-    )
-    pop_params = PopulationParams(min_pop_size=5, generation_size=15)
-    nb_params = NeighbourhoodParams(
-        weight_wait_time=5, weight_time_warp=18, nb_granular=25
+    pop_params = PopulationParams(
+        min_pop_size=5, generation_size=3, nb_elite=2, nb_close=2
     )
+    nb_params = NeighbourhoodParams(nb_granular=20)
 
     data = instance2data(instance)
     rng = RandomNumberGenerator(seed=seed)
-    pen_manager = PenaltyManager(pen_params)
+    pen_manager = PenaltyManager()
     pop = Population(bpd, params=pop_params)
 
     neighbours = compute_neighbours(data, nb_params)
     ls = LocalSearch(data, rng, neighbours)
 
-    node_ops = [Exchange10, Exchange11, TwoOpt]
+    node_ops = [Exchange10, TwoOpt]
     for node_op in node_ops:
         ls.add_node_operator(node_op(data))
 
-    route_ops = [SwapStar, SwapRoutes]
+    route_ops = [RelocateStar, SwapStar, SwapRoutes]
     for route_op in route_ops:
         ls.add_route_operator(route_op(data))
 
     init = [
         Solution.make_random(data, rng) for _ in range(pop_params.min_pop_size)
     ]
-    algo = GeneticAlgorithm(
-        data, pen_manager, rng, pop, ls, srex, init, gen_params
-    )
+    algo = GeneticAlgorithm(data, pen_manager, rng, pop, ls, srex, init)
     res = algo.run(MaxRuntime(time_limit))
 
     assert res.best.is_feasible(), "No feasible solution found."