internal static Population <List <Genome>, Problem, Fitness> InitializePopulation(
Problem problem, Second target, int population_size, int round_count)
{
IRandomGenerator random = new RandomGenerator();
// generate a list of cities to place.
List <int> cities = new List <int>();
for (int city_to_place = 0; city_to_place < problem.Cities; city_to_place++)
{
cities.Add(city_to_place);
}
// create the population
Population <List <Genome>, Problem, Fitness> population = new Population <List <Genome>, Problem, Fitness>(
null, false);
// create the fitness calculator.
FitnessCalculator fitness_calculator = new FitnessCalculator(5);
while (population.Count < population_size)
{
OsmSharp.Logging.Log.TraceEvent("OsmSharp.Math.VRP.MultiSalesman.Facade", System.Diagnostics.TraceEventType.Information,
"Initializing population individual {0}/{1}...", population.Count + 1, population_size);
// create copy of cities
List <int> cities_list = new List <int>(cities);
// create new individuals.
Individual individual =
new Individual(new List <Genome>());
// place one random city in each round.
for (int round_idx = 0; round_idx < round_count; round_idx++)
{
// select a random city to place.
int city_idx = random.Generate(cities_list.Count);
int city = cities_list[city_idx];
cities_list.RemoveAt(city_idx);
// create new genome.
Genome genome = new Genome();
genome.Add(city);
individual.Genomes.Add(genome);
}
individual = BestPlacementHelper.Do(
problem,
fitness_calculator,
individual,
cities_list);
// add inidividual to the population.
population.Add(individual);
OsmSharp.Logging.Log.TraceEvent("OsmSharp.Math.VRP.MultiSalesman.Facade", System.Diagnostics.TraceEventType.Information,
"Done!");
}
return(population);
}
/// <summary>
/// Re-places all the cities again to their own best place.
/// </summary>
/// <param name="solver"></param>
/// <param name="mutating"></param>
/// <returns></returns>
private Individual <List <int>, GeneticProblem, Fitness> MutateByRePlacement(
Solver <List <int>, GeneticProblem, Fitness> solver,
Individual <List <int>, GeneticProblem, Fitness> mutating)
{
List <int> nodes_to_re_place = mutating.Genomes.ToList <int>();
List <int> current_placement = mutating.Genomes.ToList <int>();
foreach (int node_to_place in nodes_to_re_place)
{
// take the node out.
current_placement.Remove(node_to_place);
// place the node back in.
BestPlacementHelper helper = BestPlacementHelper.Instance();
helper.Do(
solver.Problem,
solver.FitnessCalculator as FitnessCalculator,
current_placement,
node_to_place);
}
Individual individual = new Individual(current_placement);
individual.CalculateFitness(solver.Problem, solver.FitnessCalculator);
return(individual);
}
/// <summary>
/// Mutates a given individual.
/// </summary>
/// <param name="solver"></param>
/// <param name="mutating"></param>
/// <returns></returns>
public Individual <List <int>, GeneticProblem, Fitness> Mutate(
Solver <List <int>, GeneticProblem, Fitness> solver,
Individual <List <int>, GeneticProblem, Fitness> mutating)
{
// take a random piece.
int idx = solver.Random.Next(mutating.Genomes.Count);
List <int> new_genome = new List <int>(mutating.Genomes);
int customer = new_genome[idx];
new_genome.RemoveAt(idx);
// apply best placement algorithm to place the selected genomes.
BestPlacementHelper helper = BestPlacementHelper.Instance();
helper.Do(
solver.Problem,
solver.FitnessCalculator as FitnessCalculator,
new_genome,
customer);
Individual individual = new Individual(new_genome);
individual.CalculateFitness(solver.Problem, solver.FitnessCalculator);
return(individual);
}
/// <summary>
/// Generates one individual.
/// </summary>
/// <param name="solver"></param>
/// <returns></returns>
public Individual <List <Genome>, Problem, Fitness> Generate(
Solver <List <Genome>, Problem, Fitness> solver)
{
IRandomGenerator random = new RandomGenerator();
// generate a list of cities to place.
List <int> cities = new List <int>();
for (int city_to_place = 0; city_to_place < solver.Problem.Cities; city_to_place++)
{
cities.Add(city_to_place);
}
// create new individuals.
Individual individual =
new Individual(new List <Genome>());
// place one random city in each round.
for (int round_idx = 0; round_idx < solver.Problem.InitialVehicles; round_idx++)
{
// select a random city to place.
int city_idx = random.Generate(cities.Count);
int city = cities[city_idx];
cities.RemoveAt(city_idx);
// create new genome.
Genome genome = new Genome();
genome.Add(city);
individual.Genomes.Add(genome);
}
individual = BestPlacementHelper.Do(
solver.Problem,
(solver.FitnessCalculator as FitnessCalculator),
individual,
cities);
return(individual);
}