public double Evaluate(IChromosome chromosome)
{
MyProblemChromosome mpc = chromosome as MyProblemChromosome;
HashSet <int> checkDupicate = new HashSet <int>();
int count = 0;
for (int i = 0; i < mpc.Length; i++)
{
int index = (int)mpc.GetGene(i).Value;
int id = CandiatesForEachCentroid[i][index];
if (checkDupicate.Add(id))
{
count++;
}
}
mpc.Significance = count;
if (count <= CentroidPoints.Count() * 0.94)
{
return(double.MinValue);
}
double fitness = 0;
for (int i = 0; i < mpc.Length; i++)
{
var o = CentroidPoints[i];
int index = (int)mpc.GetGene(i).Value;
var d = BusStopPoints[CandiatesForEachCentroid[i][index]];
fitness += (o.X - d.X) * (o.X - d.X) + (o.Y - d.Y) * (o.Y - d.Y);
}
return(-fitness);
}
public static void CorretionCentroid()
{
//中心点数据
Stopwatch sw = new Stopwatch();
sw.Start();
List <Coordinate> centroidPoints = new List <Coordinate>(80);
StreamReader sr = new StreamReader(@"D:\MagicSong\OneDrive\2017研究生毕业设计\数据\项目用数据\中心点80.txt");
sr.ReadLine();//读取标题行
while (!sr.EndOfStream)
{
string[] line = sr.ReadLine().Split(',');
centroidPoints.Add(new Coordinate(double.Parse(line[1]), double.Parse(line[2])));
}
sr.Close();
//Bus数据,并且构造KD树
KdTree myKdtree = new KdTree(2);
IFeatureSet busFS = FeatureSet.Open(@"D:\MagicSong\OneDrive\2017研究生毕业设计\数据\项目用数据\BusStopGauss.shp");
List <Coordinate> busStopPoints = new List <Coordinate>(busFS.NumRows());
foreach (var item in busFS.Features)
{
var c = item.Coordinates[0];
busStopPoints.Add(c);
myKdtree.Insert(new double[] { c.X, c.Y }, item);
}
Console.WriteLine("数据读取完毕，开始构造遗传算法");
IFeatureSet newCentroid = new FeatureSet(FeatureType.Point);
newCentroid.Name = "优化过的中心点";
newCentroid.Projection = ProjectionInfo.FromEpsgCode(GAUSS_EPSG);
newCentroid.DataTable.Columns.Add("name", typeof(string));
//遗传算法，构造适应性函数
MyProblemChromosome.CandiateNumber = 5;
List <int[]> candinatesForEachControid = new List <int[]>(centroidPoints.Count);
foreach (var item in centroidPoints)
{
object[] nearest = myKdtree.Nearest(new double[] { item.X, item.Y }, MyProblemChromosome.CandiateNumber);
candinatesForEachControid.Add(nearest.Select((o) =>
{
var f = o as IFeature;
return(f.Fid);
}).ToArray());
}
MyProblemFitness fitness = new MyProblemFitness(centroidPoints, busStopPoints, candinatesForEachControid);
MyProblemChromosome mpc = new MyProblemChromosome(centroidPoints.Count);
//这边可以并行
MyProblemChromosome globalBest = null;
Console.WriteLine("遗传算法构造已经完成！");
sw.Stop();
Console.WriteLine("一共用时：{0}s", sw.Elapsed.TotalSeconds);
int GACount = 8;
Parallel.For(0, GACount, new Action <int>((index) =>
{
var selection = new EliteSelection();
var crossover = new TwoPointCrossover();
var mutation = new ReverseSequenceMutation();
var population = new Population(1000, 1200, mpc);
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
ga.Termination = new GenerationNumberTermination(1000);
Stopwatch sw1 = new Stopwatch();
sw1.Start();
Console.WriteLine("遗传算法任务{0}正在运行.......", index);
ga.Start();
var best = ga.BestChromosome as MyProblemChromosome;
if (globalBest == null || globalBest.Fitness < best.Fitness)
{
globalBest = best;
}
sw1.Stop();
Console.WriteLine("第{0}次遗传算法已经完成，耗费时间为：{1}s，最终的fitness为：{2},有效个数为：{3}", index, sw1.Elapsed.TotalSeconds, best.Fitness, best.Significance);
}));
Console.WriteLine("Final Choose!");
Console.WriteLine("最终的fitness为：{0},有效个数为：{1}", globalBest.Fitness, globalBest.Significance);
for (int i = 0; i < globalBest.Length; i++)
{
int index = candinatesForEachControid[i][(int)globalBest.GetGene(i).Value];
Coordinate c = busStopPoints[index];
var f = newCentroid.AddFeature(new Point(c));
f.DataRow.BeginEdit();
f.DataRow["name"] = busFS.GetFeature(index).DataRow["name"];
f.DataRow.EndEdit();
}
newCentroid.SaveAs("newCentroid.shp", true);
Console.ReadKey();
}
