public IActionResult LocalBeamSearchA(string stringArray, int dim, int states, int maxCounter)
{
int[] array = _IFunctions.StringToInt(stringArray, dim);
int[][] Array = _IFunctions.array1Dto2D(array, dim);
State[] BestStates = new State[states];
for (int i = 0; i < states; i++) //generating k random states
{
BestStates[i] = new State(dim);
BestStates[i].niz = _IFunctions.GenerateArray(dim);
BestStates[i].h = _IFunctions.Heuristic(BestStates[i].niz, dim);
}
bool change = true;
while (change) //sort initial states
{
change = false;
for (int i = 0; i < states - 1; i++)
{
if (BestStates[i].h > BestStates[i + 1].h)
{
State s = BestStates[i];
BestStates[i] = BestStates[i + 1];
BestStates[i + 1] = s;
change = true;
}
}
}
State[] allStates = new State[states * states];
int counter2 = 0;
while (BestStates[0].h != 0 && counter2 < maxCounter) // do until we find the state with h0, or until the counter reaches final status
{
int counter = 0;
for (int i = 0; i < states; i++)
{
for (int j = 0; j < states; j++)
{
allStates[counter] = new State(dim);
allStates[counter].niz = _IFunctions.rndSuccessor(_IFunctions.CopyRow(BestStates[j].niz, dim), dim); //for k states we are making k random single moves
allStates[counter].h = _IFunctions.Heuristic(allStates[counter].niz, dim);
counter++;
}
}
change = true; //sort expanded array
while (change)
{
change = false;
for (int i = 0; i < states * states - 1; i++)
{
if (allStates[i].h > allStates[i + 1].h)
{
State s = allStates[i];
allStates[i] = allStates[i + 1];
allStates[i + 1] = s;
change = true;
}
}
}
for (int i = 0; i < states; i++) //selecting k best states
{
BestStates[i].niz = _IFunctions.CopyRow(allStates[i].niz, dim);
BestStates[i].h = _IFunctions.Heuristic(allStates[i].niz, dim);
}
counter2++;
}
LocalBeamSearchVM VM = new LocalBeamSearchVM()
{
array = _IFunctions.array2Dto1D(BestStates[0].niz, dim),
dimension = dim,
heuristic = _IFunctions.Heuristic(BestStates[0].niz, dim),
dimensions = new List <SelectListItem>()
{
new SelectListItem {
Text = "4x4", Value = "4"
},
new SelectListItem {
Text = "5x5", Value = "5"
},
new SelectListItem {
Text = "6x6", Value = "6"
},
new SelectListItem {
Text = "7x7", Value = "7"
},
new SelectListItem {
Text = "8x8", Value = "8"
},
new SelectListItem {
Text = "9x9", Value = "9"
},
new SelectListItem {
Text = "10x10", Value = "10"
},
new SelectListItem {
Text = "11x11", Value = "11"
},
new SelectListItem {
Text = "12x12", Value = "12"
},
},
counter = counter2,
nmbrStates = states,
maxCounter = maxCounter
};
return(PartialView("LocalBeamSearchDone", VM));
}
public IActionResult SimulatedAnealingA(string stringArray, int dim, int T0, int coolingFactor)
{
int[] array = _IFunctions.StringToInt(stringArray, dim);
int[][] Array = _IFunctions.array1Dto2D(array, dim);
int[][] array2;
int deltaH;
int Hs = 1;
int counter = 0;
while (T0 > 0 && Hs != 0) //do until the temperature reaches 0, or we found state with h0
{
array2 = _IFunctions.rndSuccessor(Array, dim); //make single random move
Hs = _IFunctions.Heuristic(array2, dim);
deltaH = Hs - _IFunctions.Heuristic(Array, dim);
if (deltaH < 0) //if the random state is better than previous state we use it as current state
{
Array = _IFunctions.CopyRow(array2, dim);
}
else if (Math.Pow(2.71828, -(deltaH / T0)) > new Random().NextDouble()) //if the state is worse than previous state, we calculate propability
{ //for selecting it anyway
Array = _IFunctions.CopyRow(array2, dim);
}
T0 -= coolingFactor; //decreasing temperature
counter++;
}
SimulatedAnealingVM VM = new SimulatedAnealingVM()
{
array = _IFunctions.array2Dto1D(Array, dim),
dimension = dim,
heuristic = _IFunctions.Heuristic(Array, dim),
dimensions = new List <SelectListItem>()
{
new SelectListItem {
Text = "4x4", Value = "4"
},
new SelectListItem {
Text = "5x5", Value = "5"
},
new SelectListItem {
Text = "6x6", Value = "6"
},
new SelectListItem {
Text = "7x7", Value = "7"
},
new SelectListItem {
Text = "8x8", Value = "8"
},
new SelectListItem {
Text = "9x9", Value = "9"
},
new SelectListItem {
Text = "10x10", Value = "10"
},
new SelectListItem {
Text = "11x11", Value = "11"
},
new SelectListItem {
Text = "12x12", Value = "12"
},
},
counter = counter,
T0 = T0,
coolingFactor = coolingFactor
};
return(PartialView("SimulatedAnealingDone", VM));
}
public int[][] GetBestH(int[][] array, int dim, int startH)
{
int h = startH;
int h2;
int[][] array2 = _IFunctions.CopyRow(array, dim);
int[][] nizFinal = _IFunctions.CopyRow(array, dim);
List <int> Is = new List <int>();
List <int> Js = new List <int>();
List <int> Hs = new List <int>();
for (int i = 0; i < dim; i++)
{
for (int j = 0; j < dim; j++)
{
if (array[i][j] != 1) //searching space where the queen is not in for each column
{
array2[i][j] = 1; //setting queen in new position
for (int k = 0; k < dim; k++)
{
if (array2[k][j] == 1 && i != k)
{
array2[k][j] = 0; //remove old position for queen
break;
}
}
h2 = _IFunctions.Heuristic(array2, dim); //Calculate heurstic for array with new position of queen
if (h2 < h)
{
h = h2;
nizFinal = _IFunctions.CopyRow(array2, dim);
}
else if (h2 == h)
{
Is.Add(i);
Js.Add(j);
Hs.Add(h2);
}
array2 = _IFunctions.CopyRow(array, dim);
}
}
}
if (h >= startH) // if there are states with same heuristic we randomly pick state
{
for (int i = 0; i < Hs.Count(); i++)
{
if (Hs[i] != h)
{
Is.RemoveAt(i);
Js.RemoveAt(i);
Hs.RemoveAt(i);
}
}
int rnd = new Random().Next(0, Is.Count());
for (int i = 0; i < dim; i++)
{
if (nizFinal[i][Js[rnd]] == 1)
{
nizFinal[i][Js[rnd]] = 0;
break;
}
}
nizFinal[Is[rnd]][Js[rnd]] = 1;
}
return(nizFinal);
}