private void tsbOpen_Click(object sender, EventArgs e)
{
TSPBenchmarkProblem.ImportATSPFile(true, true); //from reference imported
splitContainer2.Panel1.Invalidate(); //or refresh
//reset pheromone map allocate memory
pheromones = new double[TSPBenchmarkProblem.numberOfObjects, TSPBenchmarkProblem.numberOfObjects]; //numberOfObjects = cities
probability = new double[TSPBenchmarkProblem.numberOfObjects];
candidates = new int[TSPBenchmarkProblem.numberOfObjects];
txtKnownOptimalObj.Text = TSPBenchmarkProblem.minimalDistance.ToString();
labBestRoute.Text = "Best Route ( Number of cities: " + TSPBenchmarkProblem.numberOfObjects + " )";
btnReset.Enabled = true;
}
private void tsbOpen_Click(object sender, EventArgs e)
{
TSPBenchmarkProblem.ImportATSPFile(true, true); //from reference imported
soFarTheBestRoute = new int[TSPBenchmarkProblem.numberOfObjects];
splitContainer2.Panel1.Invalidate(); //or refresh
numberOfCities = TSPBenchmarkProblem.numberOfObjects;
if (TSPBenchmarkProblem.minimalDistance == 0)
{
txtKnownOptimalObj.Text = "n/a";
}
else
{
txtKnownOptimalObj.Text = TSPBenchmarkProblem.minimalDistance.ToString();
}
labBestRoute.Text = "Best Route ( Number of cities: " + TSPBenchmarkProblem.numberOfObjects + " )";
btnReset.Enabled = true;
}
private void ACOSolution_Paint(object sender, PaintEventArgs e)
{
TSPBenchmarkProblem.DrawCitesAndARoute(e.Graphics, ACO.Width, ACO.Height, null);
if (theSolver != null | thePermutationGA != null)
{
if (rbtACOSolver.Checked)
{
TSPBenchmarkProblem.DrawCitesAndARoute(e.Graphics, ACO.Width,
ACO.Height, theSolver.SoFarTheBestSolution);
}
else
{
TSPBenchmarkProblem.DrawCitesAndARoute(e.Graphics, ACO.Width,
ACO.Height, thePermutationGA.SoFarTheBestSolution);
}
}
}
private void tsbOpen_Click(object sender, EventArgs e)
{
theSolver = null;
thePermutationGA = null;
//open
int status = TSPBenchmarkProblem.ImportATSPFile(true, true);
if (status == -1)
{
return;
}
ACO.Refresh();
title.Text = $"Title: { TSPBenchmarkProblem.Name }";
numCities.Text = $"Number Of Cities: { TSPBenchmarkProblem.NumberOfCities }";
ShortestLen.Text = $"Global Shortest Length: { TSPBenchmarkProblem.OptimumObjective:0.00}";
txtGreedy.Text = $"Greedy Route Length: {TSPBenchmarkProblem.ComputeRouteLength(TSPBenchmarkProblem.GetGreedyShortestRoute())}";
rtbDataDescription.Text = TSPBenchmarkProblem.Comment;
int[] cities = new int[TSPBenchmarkProblem.NumberOfCities];
cities = TSPBenchmarkProblem.OptimalTour;
if (cities != null)
{
rtbShort.AppendText("Global Shortest Route: \n");
for (int c = 0; c < TSPBenchmarkProblem.NumberOfCities; c++)
{
rtbShort.AppendText($"{cities[c]} ");
}
}
cities = TSPBenchmarkProblem.GetGreedyShortestRoute();
if (cities != null)
{
rtbGreedy.AppendText("Greedy Shortest Route: \n");
for (int c = 0; c < TSPBenchmarkProblem.NumberOfCities; c++)
{
rtbGreedy.AppendText($"{cities[c]} ");
}
}
rbtCreateGA.Enabled = true;
rbtACOSolver.Enabled = true;
btnReset.Enabled = true;
}
private void splitContainer2_Panel1_Paint(object sender, PaintEventArgs e) //事件
{
TSPBenchmarkProblem.DrawCitiesOptimalRouteAndARoute(e.Graphics, e.ClipRectangle.Width, e.ClipRectangle.Height, soFarTheBestRoute); //都藏在e裡
}
private void RunOneIteration()
{
iterationBestObj = 1000000000;
double iterationAverageObj = 0.0;
int iterationBestAnt = 0;
// all ants create their solution
for (int k = 0; k < numberOfAnts; k++)
{
// initialize candidate set
for (int j = 0; j < candidates.Length; j++)
{
candidates[j] = j;
}
candidateNumber = TSPBenchmarkProblem.numberOfObjects;
// First city is randomly selected
ants[k][0] = rnd.Next(TSPBenchmarkProblem.numberOfObjects);
int istar = ants[k][0];
int istarID = 0;
// remove istar from candidates and pack candidates
//find istar ID
for (int i = 0; i < candidateNumber; i++)
{
if (istar == candidates[i])
{
istarID = i;
}
}
//istar ID 後的全部往前移
for (int i = istarID; i < candidateNumber - 1; i++)
{
candidates[i] = candidates[i + 1];
}
candidateNumber--;
//往下走
int jstar = istar;
for (int j = 1; j < TSPBenchmarkProblem.numberOfObjects; j++)
{
for (int i = 0; i < candidateNumber; i++)
{
probability[i] = Math.Pow(pheromones[jstar, candidates[i]], alpha) *
Math.Pow(1.0 / TSPBenchmarkProblem.fromToMatrix[jstar, candidates[i]], beta);
}
// get next jstar
int candidateID = 0;
if (radiobtnDeterministic.Checked == true)
{
candidateID = DeterministicSelect(probability, candidateNumber);
}
else if (radiobtnStochastic.Checked == true)
{
candidateID = StochasticSelect(probability, candidateNumber);
}
jstar = candidates[candidateID];
ants[k][j] = jstar;
int jstarID = 0;
// remove jstar from candidates and pack candidates
//find jstar ID
for (int i = 0; i < candidateNumber; i++)
{
if (jstar == candidates[i])
{
jstarID = i;
}
}
//jstar ID 後的全部往前移
for (int i = jstarID; i < candidateNumber - 1; i++)
{
candidates[i] = candidates[i + 1];
}
candidateNumber--;
}
// evaluate objectives
objectives[k] = TSPBenchmarkProblem.ComputeObjectiveValue(ants[k]);
iterationAverageObj += objectives[k];
// update iteration best
if (objectives[k] < iterationBestObj)
{
iterationBestObj = objectives[k];
iterationBestAnt = k;
}
// update pheromone map
for (int j = 0; j < TSPBenchmarkProblem.numberOfObjects - 1; j++)
{
int r = ants[k][j];
int c = ants[k][j + 1];
pheromones[r, c] += droppingAmount / objectives[k];
}
for (int r = 0; r < TSPBenchmarkProblem.numberOfObjects; r++)
{
for (int c = 0; c < TSPBenchmarkProblem.numberOfObjects; c++)
{
pheromones[r, c] *= evaporateRate;
}
}
funtionCallCount++;
txtNumberOfFunctionCalls.Text = funtionCallCount.ToString();
}
//update so far the best
if (iterationBestObj < soFarTheBestObj)
{
soFarTheBestObj = iterationBestObj;
for (int j = 0; j < TSPBenchmarkProblem.numberOfObjects; j++)
{
soFarTheBestRoute[j] = ants[iterationBestAnt][j];
}
soFarTheBestInterationID = iterationCount;
txtSoFarTheBest.Text = soFarTheBestObj.ToString();
//畫圖
splitContainer2.Panel1.Refresh();
}
else
{
iterationWithoutImprovementCount++;
}
//畫線
iterationAverageObj /= numberOfAnts;
averageLine.Points.AddXY(iterationCount, iterationAverageObj);
iterationBestLine.Points.AddXY(iterationCount, iterationBestObj);
soFarTheBestLine.Points.AddXY(iterationCount, soFarTheBestObj);
}
void ResetACO()
{
//clear
iterationCount = 0;
iterationWithoutImprovementCount = 0;
funtionCallCount = 0;
chartObj.Series.Clear();
//get UI
numberOfAnts = Convert.ToInt16(txtNumberOfAnts.Text);
alpha = Convert.ToDouble(txtPheromoneFacter.Text);
beta = Convert.ToDouble(txtHeuristicFactor.Text);
initialValueOfPheromome = Convert.ToDouble(txtInitialPheromoneValue.Text);
evaporateRate = Convert.ToDouble(txtEvaporationRate.Text);
droppingAmount = Convert.ToDouble(txtDroppingAmount.Text);
//setUI
txtSoFarTheBest.Text = "";
txtIterationID.Text = "0";
txtNumberOfFunctionCalls.Text = "0";
txtAtIteration.Text = "0";
txtNumberOfFunctionCalls.Text = "0";
//initailize pheromone map
for (int i = 0; i < pheromones.GetLength(0); i++)
{
for (int j = 0; j < pheromones.GetLength(1); j++)
{
pheromones[i, j] = initialValueOfPheromome;
}
}
//allocate ants
ants = new int[numberOfAnts][];
objectives = new double[numberOfAnts];
for (int i = 0; i < numberOfAnts; i++)
{
ants[i] = new int[TSPBenchmarkProblem.numberOfObjects];
for (int j = 0; j < TSPBenchmarkProblem.numberOfObjects; j++)
{
ants[i][j] = j;
}
shuffleRoute(ants[i]);
objectives[i] = TSPBenchmarkProblem.ComputeObjectiveValue(ants[i]);
}
//so far the best route
iterationBestObj = 100000000;
int bestID = 0;
for (int i = 0; i < numberOfAnts; i++)
{
if (objectives[i] < iterationBestObj)
{
objectives[i] = iterationBestObj;
bestID = i;
}
}
soFarTheBestObj = iterationBestObj;
soFarTheBestRoute = new int[TSPBenchmarkProblem.numberOfObjects];
for (int j = 0; j < TSPBenchmarkProblem.numberOfObjects; j++)
{
soFarTheBestRoute[j] = ants[bestID][j];
}
//txtSoFarTheBest.Text = soFarTheBestObj.ToString();
//chart setting
averageLine = new Series();
averageLine.ChartType = SeriesChartType.Line;
averageLine.Name = "Average";
iterationBestLine = new Series();
iterationBestLine.ChartType = SeriesChartType.Line;
iterationBestLine.Name = "Iteration Best";
soFarTheBestLine = new Series();
soFarTheBestLine.ChartType = SeriesChartType.Line;
soFarTheBestLine.Name = "So Far the Best";
soFarTheBestLine.BorderWidth = 3;
chartObj.Series.Add(averageLine);
chartObj.Series.Add(iterationBestLine);
chartObj.Series.Add(soFarTheBestLine);
//protection
btnRunOneIteration.Enabled = true;
btnRunToEnd.Enabled = true;
}
void ResetACO()
{
//clear
iterationCount = 0;
NoImprovementCount = 0;
chartObj.Series.Clear();
//get UI
numberOfHM = Convert.ToInt16(txtNumberOfAnts.Text);
HMCR = Convert.ToDouble(txtHMCR.Text);
PAR = Convert.ToDouble(txtPAR.Text);
//setUI
txtSoFarTheBest.Text = "";
txtIterationID.Text = "0";
txtNumberOfIterationWithoutImprovement.Text = "0";
progressBar1.Value = 0;
//allocate HM
HSforTSP = new HarmonySearch(numberOfHM, numberOfCities);
HSforTSP.iterationWithoutImprovementCount = 0;
for (int i = 0; i < numberOfHM; i++)
{
for (int j = 0; j < numberOfCities; j++)
{
HSforTSP.HM[i][j] = rnd.NextDouble(); //random 0~1
}
}
//temp = 排序用
double[][] temp = new double[numberOfHM][];
for (int i = 0; i < numberOfHM; i++)
{
temp[i] = new double[numberOfCities];
for (int j = 0; j < numberOfCities; j++)
{
temp[i][j] = HSforTSP.HM[i][j];
}
}
//initialize realRoute
realRoute = new int[numberOfHM][];
for (int i = 0; i < numberOfHM; i++)
{
realRoute[i] = new int[numberOfCities];
for (int j = 0; j < numberOfCities; j++)
{
realRoute[i][j] = j;
}
}
//convert to real route and calculate evaluation in this step
for (int i = 0; i < numberOfHM; i++)
{
Array.Sort(temp[i], realRoute[i]);
HSforTSP.evaluation[i] = TSPBenchmarkProblem.ComputeObjectiveValue(realRoute[i]);
}
HSforTSP.CountPenalty(500);
//so far the best route
HSforTSP.Sort();
soFarTheBestObj = HSforTSP.evaluation[HSforTSP.BestID];
soFarTheBestRoute = new int[TSPBenchmarkProblem.numberOfObjects];
for (int j = 0; j < TSPBenchmarkProblem.numberOfObjects; j++)
{
soFarTheBestRoute[j] = realRoute[HSforTSP.BestID][j];
}
//chart setting
averageLine = new Series();
averageLine.ChartType = SeriesChartType.Line;
averageLine.Name = "Average";
soFarTheBestLine = new Series();
soFarTheBestLine.ChartType = SeriesChartType.Line;
soFarTheBestLine.Name = "So Far the Best";
soFarTheBestLine.BorderWidth = 3;
chartObj.Series.Add(averageLine);
chartObj.Series.Add(soFarTheBestLine);
//protection
btnRunOneIteration.Enabled = true;
btnRunToEnd.Enabled = true;
}
private void RunOneIteration()
{
//Step2 : improvise new vector
double[] newV = new double[numberOfCities];
for (int j = 0; j < numberOfCities; j++)
{
if (rnd.NextDouble() < HMCR) //有0.99的機會從中選一個
{
int selectedHM = rnd.Next(numberOfHM); //0~20
if (rnd.NextDouble() < (1 - PAR)) //有0.95的機率選一個
{
newV[j] = HSforTSP.HM[selectedHM][j];
}
else //有0.05的機率往上/往下
{
if (rnd.NextDouble() < 0.5) //有一半機率往上
{
newV[j] = HSforTSP.HM[selectedHM][j] + 1.0 / numberOfCities;
}
else //一半機率往下
{
newV[j] = HSforTSP.HM[selectedHM][j] - 1.0 / numberOfCities;
}
}
}
else
{
newV[j] = rnd.NextDouble();
}
}
//calculate the evaluation of newVector
double[] temp = new double[numberOfCities]; //排序用
int[] realRouteOfNewV = new int[numberOfCities];
for (int j = 0; j < numberOfCities; j++)
{
temp[j] = newV[j];
realRouteOfNewV[j] = j;
}
Array.Sort(temp, realRouteOfNewV);
double evaluationOfNewV = TSPBenchmarkProblem.ComputeObjectiveValue(realRouteOfNewV);
//Step 3: update HM
HSforTSP.UpdateHM(newV, evaluationOfNewV);
HSforTSP.CountPenalty(500);
//average
double iterationAverageObj = 0;
for (int i = 0; i < numberOfHM; i++)
{
iterationAverageObj += HSforTSP.evaluation[i];
}
iterationAverageObj /= numberOfHM;
//update so far the best
for (int j = 0; j < numberOfCities; j++)
{
temp[j] = HSforTSP.HM[HSforTSP.BestID][j];
soFarTheBestRoute[j] = j;
}
Array.Sort(temp, soFarTheBestRoute);
soFarTheBestObj = HSforTSP.evaluation[HSforTSP.BestID];
//UI
progressBar1.Value = iterationCount;
NoImprovementCount = HSforTSP.iterationWithoutImprovementCount;
txtNumberOfIterationWithoutImprovement.Text = NoImprovementCount.ToString();
txtIterationID.Text = iterationCount.ToString();
txtNumberOfIterationWithoutImprovement.Text = HSforTSP.iterationWithoutImprovementCount.ToString();
txtSoFarTheBest.Text = soFarTheBestObj.ToString();
//畫線
averageLine.Points.AddXY(iterationCount, iterationAverageObj);
soFarTheBestLine.Points.AddXY(iterationCount, HSforTSP.evaluation[HSforTSP.BestID]);
if (checkBoxShowAnimation.Checked == true)
{
if (iterationCount % 100 == 0)
{
splitContainer2.Panel1.Refresh();
}
txtNumberOfIterationWithoutImprovement.Refresh();
txtIterationID.Refresh();
txtSoFarTheBest.Refresh();
chartObj.Update();
}
}
private void Greedy_Paint(object sender, PaintEventArgs e)
{
TSPBenchmarkProblem.DrawCitiesOptimalRouteAndARoute(e.Graphics, Greedy.Width,
Greedy.Height, TSPBenchmarkProblem.GetGreedyShortestRoute());
}
private void Optimal_Paint(object sender, PaintEventArgs e)
{
TSPBenchmarkProblem.DrawCitiesOptimalRouteAndARoute(e.Graphics, Optimal.Width,
Optimal.Height, TSPBenchmarkProblem.OptimalTour);
}