// selects the best element of the small and the big population together and sets global.solution to it's solution
// then removes the best element
public PopulationElement SelectBestElement(Boolean delete)
{
Base global = Base.GetInstance();
PopulationElement bestElement = new PopulationElement();
if (global.populationLarge.Count > 0)
{
bestElement = global.populationLarge[0];
}
Boolean bestFoundInSmall = false;
int positionBestFound = 0;
if(global.Verschnittoptimierung.radioButton_selMuePlusLambda.Checked || global.populationLarge.Count == 0)
{
bestElement = global.populationSmall[0];
bestFoundInSmall = true;
positionBestFound = 0;
for (int i = 0; i < global.populationSmall.Count; i++)
{
if (global.populationSmall[i].fitnessValue < bestElement.fitnessValue)
{
bestElement = global.populationSmall[i];
bestFoundInSmall = true;
positionBestFound = i;
}
}
}
for(int i = 0; i < global.populationLarge.Count; i++)
{
if(global.populationLarge[i].fitnessValue < bestElement.fitnessValue)
{
bestElement = global.populationLarge[i];
bestFoundInSmall = false;
positionBestFound = i;
}
}
if(delete && bestFoundInSmall)
{
global.populationSmall.RemoveAt(positionBestFound);
}
if(delete && !bestFoundInSmall)
{
global.populationLarge.RemoveAt(positionBestFound);
}
return (bestElement);
}
public void BombingAlgorithm()
{
Base global = Base.GetInstance();
ClassificationNumbers classificationNumbers = new ClassificationNumbers(global);
if(global.runningProcess.firstStep)
{
global.changeCounter = 0;
}
// preparations
global.runningProcess.state = 1;
Tools tools = new Tools();
// display running process display
global.Verschnittoptimierung.processRunning_gear.Visible = true;
global.Verschnittoptimierung.processRunning_label.Visible = true;
// change "true" to an abort requirement, for example "best solution better than 95%"
// best solution = global.solution (is set after each step/evolutionary step)
int rim = 20;
try
{
rim = Convert.ToInt32(global.Verschnittoptimierung.evAlg_numberMaxIterations.Value);
}
catch(Exception ex)
{
rim = 20;
}
while (rim > 0 && global.changeCounter < 10)
{
// creating a basic population
if (global.runningProcess.firstStep)
{
global.changeCounter = 0;
global.evolutionStep = 0;
tools.CleanFitnessChart();
global.populationSmall = new List<PopulationElement>();
List<int> greediesForRand = new List<int>();
if (!global.tournamentPopulation)
{
greediesForRand = tools.CloneList(global.chosenGreedies);
}
else
{
greediesForRand = GetTournamentGreedies(global.mue);
}
// set tournament methods too
if(global.tournamentGreediesOnly)
{
global.tournamentGreedyMethods = new List<int>();
int number = (global.mue > global.multForLambda) ? global.multForLambda : global.multForLambda;
global.tournamentGreedyMethods = GetTournamentGreedies(number);
}
for(int i = 0; i < global.mue; i++)
{
PopulationElement element = new PopulationElement();
Solution solution = tools.CloneSolution(global.emptySolution);
// select random greedy from the selectedGreedies and set its identifier, i.e. "1" for greedy1, in global
int selectedGreedyPosition = global.random.Next(0, greediesForRand.Count);
int selectedGreedy = greediesForRand[selectedGreedyPosition];
greediesForRand.RemoveAt(selectedGreedyPosition);
global.selectedGreedy = selectedGreedy;
// execute the greedy
Fill fill = new Fill();
element.solution = fill.Greedy(true, solution);
element.fitnessValue = tools.CalculateFitness(element.solution);
global.populationSmall.Add(element);
}
EndStepBombingAlgorithm();
global.runningProcess.firstStep = false;
if (global.runningProcess.stepType == 0)
{
global.runningProcess.state = 0;
break;
}
}
// creating a new population (lambda)
for(int i = 0; i < global.populationSmall.Count; i ++)
{
// clone the population element's solution
Solution newSolutionBase = tools.CloneSolution(global.populationSmall[i].solution);
// create a ranking of the boards of this solution
List<int> rankedBoards = CreateBoardRanking(newSolutionBase);
// remove rects according to the mutation rate
float mutationRate = global.mutationRate;
while(mutationRate >= 1)
{
// remove all rects from the worst board and add to collectionBoard
// while worst board isn't empty
while(newSolutionBase.BoardList[rankedBoards[rankedBoards.Count - 1]].RectList.Count > 0)
{
Rect rectToRemove = newSolutionBase.BoardList[rankedBoards[rankedBoards.Count - 1]].RectList[0];
newSolutionBase.BoardList[rankedBoards[rankedBoards.Count - 1]].RectList.RemoveAt(0);
newSolutionBase.BoardList[newSolutionBase.BoardList.Count - 1].RectList.Add(rectToRemove);
}
// remove board which was emptied from the ranked boards (so that the new last element is the weakest board again)
rankedBoards.RemoveAt(rankedBoards.Count - 1);
mutationRate -= 1;
}
if(mutationRate > 0)
{
// calculate how many have to be removed
int nrRectsOnBoard = newSolutionBase.BoardList[rankedBoards[rankedBoards.Count - 1]].RectList.Count;
int numbersToRemove = Convert.ToInt32(Math.Floor(nrRectsOnBoard * mutationRate));
// remove the rects left to remove (starting from the last added rect)
while(numbersToRemove > 0)
{
Rect rectToRemove = newSolutionBase.BoardList[rankedBoards[rankedBoards.Count - 1]].RectList[
newSolutionBase.BoardList[rankedBoards[rankedBoards.Count - 1]].RectList.Count - 1];
newSolutionBase.BoardList[rankedBoards[rankedBoards.Count - 1]].RectList.RemoveAt(
newSolutionBase.BoardList[rankedBoards[rankedBoards.Count - 1]].RectList.Count - 1);
newSolutionBase.BoardList[newSolutionBase.BoardList.Count - 1].RectList.Add(rectToRemove);
numbersToRemove--;
}
}
// fill with a random greedy from the selectedGreedies list
// and add to large population
for (int j = 0; j < global.multForLambda; j++)
{
Solution newSolution = tools.CloneSolution(newSolutionBase);
List<int> greediesForRand = new List<int>();
if (!global.tournamentGreediesOnly)
{
greediesForRand = tools.CloneList(global.chosenGreedies);
}
else
{
greediesForRand = tools.CloneList(global.tournamentGreedyMethods);
}
// select random greedy from the selectedGreedies and set its identifier, i.e. "1" for greedy1, in global
int selectedGreedyPosition = global.random.Next(0, greediesForRand.Count);
int selectedGreedy = greediesForRand[selectedGreedyPosition];
greediesForRand.RemoveAt(selectedGreedyPosition);
global.selectedGreedy = selectedGreedy;
// execute the greedy
PopulationElement element = new PopulationElement();
Fill fill = new Fill();
element.solution = fill.Greedy(true, newSolution);
element.fitnessValue = tools.CalculateFitness(element.solution);
global.populationLarge.Add(element);
}
}
// old (small) population still existing, new (large) population (lambda) created
// -> end step if singleStep
EndStepBombingAlgorithm();
if(global.runningProcess.stepType == 0)
{
global.runningProcess.state = 0;
rim--;
break;
}
rim--;
}
if (global.changeCounter == 10 || rim == 0)
{
global.runningProcess.existing = false;
global.mue = 0;
global.lambda = 0;
}
}
