/// <summary>
/// Places a city into an idividual.
/// </summary>
/// <param name="problem"></param>
/// <param name="calculator"></param>
/// <param name="round_idx"></param>
/// <param name="individual"></param>
/// <param name="city_to_place"></param>
/// <returns></returns>
internal static BestPlacementResult CalculateBestPlacementInIndividual(
Problem problem,
FitnessCalculator calculator,
int round_idx,
Individual <List <Genome>, Problem, Fitness> individual,
int city_to_place)
{
// if the target round is empty best placement is impossible.
Genome round = individual.Genomes[round_idx];
// do best placement in the genome/round.
BestPlacementResult result =
BestPlacementHelper.CalculateBestPlacementInGenome(problem, calculator, round, city_to_place);
// set the round index.
result.RoundIdx = round_idx;
if (!individual.FitnessCalculated)
{
individual.CalculateFitness(
problem, calculator);
}
result.Fitness = calculator.Adjust(
problem,
individual.Fitness,
round_idx,
result.Increase);
// return the result.
return(result);
}
/// <summary>
/// Places cities into an individual.
/// </summary>
/// <param name="problem"></param>
/// <param name="calculator"></param>
/// <param name="genomes"></param>
/// <param name="cities_to_place"></param>
/// <returns></returns>
public static BestPlacementResult CalculateBestPlacementInGenomes(
Problem problem,
FitnessCalculator calculator,
List <Genome> genomes,
List <int> cities_to_place)
{
// initialize the best placement result.
BestPlacementResult result = null;
// place all the cities until there are no more left.
// try to place every city in every round.
for (int round_idx = 0; round_idx < genomes.Count; round_idx++)
{
Genome genome = genomes[round_idx];
BestPlacementResult new_result =
BestPlacementHelper.CalculateBestPlacementInGenome(
problem,
calculator,
genome,
cities_to_place);
if (result == null ||
new_result.Increase < result.Increase)
{
result = new_result;
result.RoundIdx = round_idx;
}
}
// return the result.
return(result);
}
/// <summary>
/// Places cities into an individual.
/// </summary>
/// <param name="problem"></param>
/// <param name="calculator"></param>
/// <param name="individual"></param>
/// <param name="cities_to_place"></param>
/// <returns></returns>
internal static BestPlacementResult CalculateBestPlacementInIndividual(
Problem problem,
FitnessCalculator calculator,
Individual <List <Genome>, Problem, Fitness> individual,
List <int> cities_to_place)
{
// initialize the best placement result.
BestPlacementResult result = null;
// place all the cities until there are no more left.
// try to place every city in every round.
foreach (int city_to_place in cities_to_place)
{
for (int round_idx = 0; round_idx < individual.Genomes.Count; round_idx++)
{
BestPlacementResult new_result =
BestPlacementHelper.CalculateBestPlacementInIndividual(
problem,
calculator,
round_idx,
individual,
city_to_place);
if (result == null ||
new_result.Fitness < result.Fitness)
{
result = new_result;
}
}
}
// return the result.
return(result);
}
/// <summary>
/// Places the given city in the individual.
/// </summary>
/// <param name="problem"></param>
/// <param name="calculator"></param>
/// <param name="genomes"></param>
/// <param name="city_to_place"></param>
/// <returns></returns>
public static BestPlacementResult CalculateBestPlacementInGenomes(
Problem problem,
FitnessCalculator calculator,
List <Genome> genomes,
int city_to_place)
{
List <int> cities_to_place = new List <int>();
cities_to_place.Add(city_to_place);
return(BestPlacementHelper.CalculateBestPlacementInGenomes(problem, calculator, genomes, cities_to_place));
}
public static Genome DoFast(
Problem problem,
FitnessCalculator calculator,
Genome genome,
List <int> cities_to_place)
{
List <Genome> genomes = new List <Genome>();
genomes.Add(genome);
return(BestPlacementHelper.DoFast(
problem,
calculator,
genomes,
cities_to_place)[0]);
}
public static List <Genome> DoFast(
Problem problem,
FitnessCalculator calculator,
List <Genome> genomes,
List <int> cities_to_place)
{
// initialize the best placement result.
BestPlacementResult result = null;
// create the new individual.
cities_to_place = new List <int>(cities_to_place);
while (cities_to_place.Count > 0)
{
int city_to_place = cities_to_place[cities_to_place.Count - 1];
cities_to_place.RemoveAt(cities_to_place.Count - 1);
// calculates the next best position for placement.
result = BestPlacementHelper.CalculateBestPlacementInGenomes(
problem,
calculator,
genomes,
city_to_place);
// calculate the new individual using the result.
List <int> round = genomes[result.RoundIdx];
if (round.Count == result.CityIdx)
{
round.Add(result.City);
}
else
{
round.Insert(result.CityIdx, result.City);
}
// remove the placed city.
cities_to_place.Remove(result.City);
}
// return the result.
return(genomes);
}
internal static BestPlacementResult CalculateBestPlacementInGenome(
Problem problem,
FitnessCalculator calculator,
Genome genome,
List <int> cities_to_place)
{
// initialize the best placement result.
BestPlacementResult result
= new BestPlacementResult();
result.RoundIdx = -1;
// try and place all cities.
for (int city_idx = 0; city_idx < cities_to_place.Count; city_idx++)
{
// try to place first city.
int city = cities_to_place[city_idx];
// place the city and check the result.
BestPlacementResult current_result =
BestPlacementHelper.CalculateBestPlacementInGenome(
problem,
calculator,
genome,
city);
// check current result
//if (result.Increase == null ||
// current_result.Increase < result.Increase)
if (current_result.Increase < result.Increase)
{
result = current_result;
}
}
return(result);
}
