/**
* This method returns the TSPState of the element with the minimum value and removes it from the queue.
* Time Complexity: O(log(|V|)) because removing a node is constant time as we have its position in
* the queue, then to read just the heap we just bubble up the min value which takes as long as
* the depth of the tree which is log(|V|), where |V| is the number of nodes
* Space Complexity: O(1) because we don't create any extra variables that vary with the size of the input.
*/
public TSPState deleteMin()
{
// grab the node with min value which will be at the root
TSPState minValue = states[1];
states[1] = states[count];
count--;
// fix the heap
int indexIterator = 1;
while (indexIterator <= count)
{
// grab left child
int smallerElementIndex = 2 * indexIterator;
// if child does not exist, break
if (smallerElementIndex > count)
{
break;
}
// if right child exists and is of smaller value, pick it
if (smallerElementIndex + 1 <= count && states[smallerElementIndex + 1].getPriority() < states[smallerElementIndex].getPriority())
{
smallerElementIndex++;
}
if (states[indexIterator].getPriority() > states[smallerElementIndex].getPriority())
{
// set the node's value to that of its smaller child
TSPState temp = states[smallerElementIndex];
states[smallerElementIndex] = states[indexIterator];
states[indexIterator] = temp;
}
indexIterator = smallerElementIndex;
}
// return the min value
return(minValue);
}
/**
* This method updates the nodes in the queue after inserting a new node
* Time Complexity: O(log(|V|)) as reording the heap works by bubbling up the min value to the top
* which takes as long as the depth of the tree which is log|V|.
* Space Complexity: O(1) as it does not create any extra variables that vary with the size of the input.
*/
public void insert(TSPState newState)
{
// update the count
count++;
states[count] = newState;
if (count > maxNum)
{
maxNum = count;
}
// as long as its parent has a larger value and have not hit the root
int indexIterator = count;
while (indexIterator > 1 && states[indexIterator / 2].getPriority() > states[indexIterator].getPriority())
{
// swap the two nodes
TSPState temp = states[indexIterator / 2];
states[indexIterator / 2] = states[indexIterator];
states[indexIterator] = temp;
indexIterator /= 2;
}
}
//**********************************************BBAlgorithm*********************************************************************************
/// <summary>
/// performs a Branch and Bound search of the state space of partial tours
/// stops when time limit expires and uses BSSF as solution
/// </summary>
/// <returns>results array for GUI that contains three ints: cost of solution, time spent to find solution, number of solutions found during search (not counting initial BSSF estimate)</returns>
public string[] bBSolveProblem()
{
string[] results = new string[3];
// TODO: Add your implementation for a branch and bound solver here.
//Initalize variables. Takes O(1) space and time
int numOfCitiesLeft = Cities.Length;
int numOfSolutions = 0;
int numOfStatesCreated = 0;
int numOfStatesNotExpanded = 0;
//Initalize time variable for stopping the algorithm after the default of 60 seconds. Takes O(1) space and time
DateTime start = DateTime.Now;
DateTime end = start.AddSeconds(time_limit / 1000);
//Create initial root state and set its priority to its lower bound. Takes O(n^2) space and time as discussed above
TSPState initialState = createInitialState();
numOfStatesCreated++;
initialState.setPriority(calculateKey(numOfCitiesLeft - 1, initialState.getLowerBound()));
//Create initial BSSF greedily
double bssfBound = createGreedyBssf();
PriorityQueue queue = new PriorityQueue(Cities.Length);
queue.insert(initialState);
// Branch and Bound until the queue is empty, we have exceeded the time limit, or we found the optimal solution
/* This loop will have a iterate 2^n times approximately with expanding and pruning for each state, then for each state it
* does O(n^2) work by reducing the matrix, so over all O((n^2)*(2^n)) time and space as well as it creates a nxn
* matrix for each state*/
while (!queue.isEmpty() && DateTime.Now < end && queue.getMinLB() != bssfBound)
{
// Grab the next state in the queue
TSPState currState = queue.deleteMin();
// check if lower bound is less than the BSSF, else prune it
if (currState.getLowerBound() < bssfBound)
{
// Branch and create the child states
for (int i = 0; i < Cities.Length; i++)
{
// First check that we haven't exceeded the time limit
if (DateTime.Now >= end)
{
break;
}
// Make sure we are only checking cities that we haven't checked already
if (currState.getPath().Contains(Cities[i]))
{
continue;
}
// Create the State
double[,] oldCostMatrix = currState.getCostMatrix();
double[,] newCostMatrix = new double[Cities.Length, Cities.Length];
// Copy the old array in the new one to modify the new without affecting the old
for (int k = 0; k < Cities.Length; k++)
{
for (int l = 0; l < Cities.Length; l++)
{
newCostMatrix[k, l] = oldCostMatrix[k, l];
}
}
City lastCityinCurrState = (City)currState.getPath()[currState.getPath().Count - 1];
double oldLB = currState.getLowerBound();
setUpMatrix(ref newCostMatrix, Array.IndexOf(Cities, lastCityinCurrState), i, ref oldLB);
double newLB = oldLB + reduceMatrix(ref newCostMatrix);
ArrayList oldPath = currState.getPath();
ArrayList newPath = new ArrayList();
foreach (City c in oldPath)
{
newPath.Add(c);
}
newPath.Add(Cities[i]);
TSPState childState = new TSPState(ref newPath, ref newLB, ref newCostMatrix);
numOfStatesCreated++;
// Prune States larger than the BSSF
if (childState.getLowerBound() < bssfBound)
{
City firstCity = (City)childState.getPath()[0];
City lastCity = (City)childState.getPath()[childState.getPath().Count - 1];
double costToLoopBack = lastCity.costToGetTo(firstCity);
// If we found a solution and it goes back from last city to first city
if (childState.getPath().Count == Cities.Length && costToLoopBack != double.MaxValue)
{
childState.setLowerBound(childState.getLowerBound() + costToLoopBack);
bssf = new TSPSolution(childState.getPath());
bssfBound = bssf.costOfRoute();
numOfSolutions++;
numOfStatesNotExpanded++; // this state is not expanded because it is not put on the queue
}
else
{
// Set the priority for the state and add the new state to the queue
numOfCitiesLeft = Cities.Length - childState.getPath().Count;
childState.setPriority(calculateKey(numOfCitiesLeft, childState.getLowerBound()));
queue.insert(childState);
}
}
else
{
numOfStatesNotExpanded++; // States that are pruned are not expanded
}
}
}
currState = null;
}
numOfStatesNotExpanded += queue.getSize(); // if the code terminated before queue is empty, then those states never got expanded
Console.WriteLine("Number of states generated: " + numOfStatesCreated);
Console.WriteLine("Number of states not Expanded: " + numOfStatesNotExpanded);
Console.WriteLine("Max Number of states put in queue: " + queue.getMaxNumOfItems());
end = DateTime.Now;
TimeSpan diff = end - start;
double seconds = diff.TotalSeconds;
results[COST] = System.Convert.ToString(bssf.costOfRoute()); // load results into array here, replacing these dummy values
results[TIME] = System.Convert.ToString(seconds);
results[COUNT] = System.Convert.ToString(numOfSolutions);
return(results);
}
