public DiscreteTS4QAP(QAPInstance instance, double rclTreshold,
int tabuListLength, int neighborChecks)
: base(tabuListLength, neighborChecks)
{
Instance = instance;
RclTreshold = rclTreshold;
}
public DiscreteSA4QAP(QAPInstance instance, int initialSolutions,
int levelLength, double tempReduction)
: base(initialSolutions, levelLength, tempReduction)
{
Instance = instance;
generatedSolutions = 0;
}
public MaxMinAntSystem2OptBest4QAP(QAPInstance instance, int numberAnts, double rho,
double alpha, double beta, int maxReinit)
: base(instance.NumberFacilities, QAPUtils.Fitness(instance, QAPUtils.RandomSolution(instance)),
numberAnts, rho, alpha, beta, maxReinit)
{
Instance = instance;
}
public DiscretePSO4QAP(QAPInstance instance, int partsCount, double prevConf,
double neighConf, int[] lowerBounds, int[] upperBounds)
: base(partsCount, prevConf, neighConf, lowerBounds, upperBounds)
{
Instance = instance;
generatedSolutions = 0;
}
public DiscreteSS4QAP(QAPInstance instance, int poolSize,
int refSetSize, double explorationFactor)
: base(poolSize, refSetSize, explorationFactor)
{
Instance = instance;
generatedSolutions = 0;
}
// Implementation of the GRC solution's construction algorithm.
public static int[] GRCSolution(QAPInstance instance, double rclThreshold)
{
int numFacilities = instance.NumberFacilities;
int[] assigment = new int[numFacilities];
int totalFacilities = numFacilities;
int index = 0;
double best = 0;
double cost = 0;
int facility = 0;
// Restricted Candidate List.
SortedList<double, int> rcl = new SortedList<double, int>();
// Available cities.
bool[] assigned = new bool[numFacilities];
assigment[0] = Statistics.RandomDiscreteUniform(0, numFacilities-1);
assigned[assigment[0]] = true;
index++;
numFacilities --;
while (numFacilities > 0) {
rcl = new SortedList<double, int>();
for (int i = 0; i < totalFacilities; i++) {
if (!assigned[i]) {
cost = 0;
for (int j = 0; j < index; j++) {
cost += instance.Distances[j,index] * instance.Flows[assigment[j],i];
}
if(rcl.Count == 0) {
best = cost;
rcl.Add(cost, i);
}
else if( cost < best) {
// The new assignment is the new best;
best = cost;
for (int j = rcl.Count-1; j > 0; j--) {
if (rcl.Keys[j] > rclThreshold * best) {
rcl.RemoveAt(j);
}
else {
break;
}
}
rcl.Add(cost, i);
}
else if (cost < rclThreshold * best) {
// The new assigment is a mostly good candidate.
rcl.Add(cost, i);
}
}
}
facility = rcl.Values[Statistics.RandomDiscreteUniform(0, rcl.Count-1)];
assigned[facility] = true;
assigment[index] = facility;
index++;
numFacilities--;
}
return assigment;
}
public DiscretePSO2OptBest4QAP(QAPInstance instance, int partsCount, double prevConf,
double neighConf, int[] lowerBounds, int[] upperBounds)
: base(partsCount, prevConf, neighConf, lowerBounds, upperBounds)
{
Instance = instance;
LocalSearchEnabled = true;
generatedSolutions = 0;
}
public DiscreteILS2OptFirst4QAP(QAPInstance instance, int restartIterations,
int[] lowerBounds, int[] upperBounds)
: base(restartIterations, lowerBounds, upperBounds)
{
Instance = instance;
RepairEnabled = true;
generatedSolutions = 0;
}
public void Start(string inputFile, string outputFile, int timeLimit)
{
QAPInstance instance = new QAPInstance(inputFile);
DiscreteSS ss = new DiscreteSS4QAP(instance, poolSize, refSetSize, explorationFactor);
ss.Run(timeLimit - timePenalty);
QAPSolution solution = new QAPSolution(instance, ss.BestSolution);
solution.Write(outputFile);
}
public void Start(string fileInput, string fileOutput, int timeLimit)
{
QAPInstance instance = new QAPInstance(fileInput);
int[] assignment = QAPUtils.GRCSolution(instance, 1.0);
QAPUtils.LocalSearch2OptBest(instance, assignment);
QAPSolution solution = new QAPSolution(instance, assignment);
solution.Write(fileOutput);
}
public DiscreteUMDA4QAP(QAPInstance instance, int popSize,
double truncFactor, int[] lowerBounds,
int[] upperBounds)
: base(popSize, truncFactor, lowerBounds, upperBounds)
{
Instance = instance;
RepairEnabled = true;
}
public DiscreteHMTSwGRASP2OptBest4QAP(QAPInstance instance, double rclThreshold,
int graspIterations, int tabuListLength,
int neighborChecks)
: base(tabuListLength, neighborChecks)
{
GRASP = new DiscreteGRASP2OptBest4QAP(instance, rclThreshold);
Instance = instance;
GRASPIterations = graspIterations;
}
public void Start(string inputFile, string outputFile, int timeLimit)
{
QAPInstance instance = new QAPInstance(inputFile);
int levelLength = (int) Math.Ceiling(levelLengthFactor * (instance.NumberFacilities * (instance.NumberFacilities - 1)));
DiscreteHMSAwGRASP2OptFirst4QAP hm = new DiscreteHMSAwGRASP2OptFirst4QAP(instance, rclTreshold, graspIterations, initialSolutions, levelLength, tempReduction);
hm.Run(timeLimit - timePenalty);
QAPSolution solution = new QAPSolution(instance, hm.BestSolution);
solution.Write(outputFile);
}
public DiscreteGA4QAP(QAPInstance instance, int popSize,
double mutationProbability,
int[] lowerBounds, int[] upperBounds)
: base(popSize, mutationProbability, lowerBounds, upperBounds)
{
Instance = instance;
RepairEnabled = true;
generatedSolutions = 0;
}
public DiscreteHMSAwGRASP2OptBest4QAP(QAPInstance instance, double rclThreshold,
int graspIterations, int initialSolutions,
int levelLength, double tempReduction)
: base(initialSolutions, levelLength, tempReduction)
{
GRASP = new DiscreteGRASP2OptBest4QAP(instance, rclThreshold);
Instance = instance;
GRASPIterations = graspIterations;
}
public void Start(string inputFile, string outputFile, int timeLimit)
{
QAPInstance instance = new QAPInstance(inputFile);
MaxMinAntSystem aco = new MaxMinAntSystem2OptBest4QAP(instance, numberAnts, rho, alpha, beta, maxReinit);
// Solving the problem and writing the best solution found.
aco.Run(timeLimit - timePenalty);
QAPSolution solution = new QAPSolution(instance, aco.BestSolution);
solution.Write(outputFile);
}
public void Start(string fileInput, string fileOutput, int timeLimit)
{
QAPInstance instance = new QAPInstance(fileInput);
int levelLength = (int) Math.Ceiling(levelLengthFactor * (instance.NumberFacilities * (instance.NumberFacilities - 1)));
DiscreteSA sa = new DiscreteSA4QAP(instance, initialSolutions, levelLength, tempReduction);
sa.Run(timeLimit - timePenalty);
QAPSolution solution = new QAPSolution(instance, sa.BestSolution);
solution.Write(fileOutput);
}
public void Start(string inputFile, string outputFile, int timeLimit)
{
QAPInstance instance = new QAPInstance(inputFile);
int neighborChecks = (int) Math.Ceiling(neighborChecksFactor * (instance.NumberFacilities * (instance.NumberFacilities - 1)));
int tabuListLength = (int) Math.Ceiling(tabuListFactor * instance.NumberFacilities);
DiscreteHMTSwGRASP2OptBest4QAP hm = new DiscreteHMTSwGRASP2OptBest4QAP(instance, rclTreshold, graspIterations, tabuListLength, neighborChecks);
hm.Run(timeLimit - timePenalty);
QAPSolution solution = new QAPSolution(instance, hm.BestSolution);
solution.Write(outputFile);
}
public static double Distance(QAPInstance instance, int[] a, int[] b)
{
double distance = 0;
for (int i = 0; i < a.Length; i++) {
if (a[i] != b[i]) {
distance += 1;
}
}
return distance;
}
public void Start(string fileInput, string fileOutput, int timeLimit)
{
QAPInstance instance = new QAPInstance(fileInput);
// Setting the parameters of the GRASP for this instance of the problem.
DiscreteGRASP grasp = new DiscreteGRASP2OptBest4QAP(instance, rclThreshold);
// Solving the problem and writing the best solution found.
grasp.Run(timeLimit - (int)timePenalty, RunType.TimeLimit);
QAPSolution solution = new QAPSolution(instance, grasp.BestSolution);
solution.Write(fileOutput);
}
public static double Fitness(QAPInstance instance, int[] assignment)
{
double cost = 0;
for (int i = 0; i < instance.NumberFacilities; i++) {
for (int j = 0; j < instance.NumberFacilities; j++) {
cost += instance.Distances[i,j] * instance.Flows[assignment[i],assignment[j]];
}
}
return cost;
}
public void Start(string inputFile, string outputFile, int timeLimit)
{
QAPInstance instance = new QAPInstance(inputFile);
int[] lowerBounds = new int[instance.NumberFacilities];
int[] upperBounds = new int[instance.NumberFacilities];
for (int i = 0; i < instance.NumberFacilities; i++) {
lowerBounds[i] = 0;
upperBounds[i] = instance.NumberFacilities - 1;
}
DiscreteILS ils = new DiscreteILS2OptFirst4QAP(instance, restartIterations, lowerBounds, upperBounds);
ils.Run(timeLimit - timePenalty);
QAPSolution solution = new QAPSolution(instance, ils.BestSolution);
solution.Write(outputFile);
}
public void Start(string fileInput, string fileOutput, int timeLimit)
{
QAPInstance instance = new QAPInstance(fileInput);
// Setting the parameters of the MA for this instance of the problem.
int[] lowerBounds = new int[instance.NumberFacilities];
int[] upperBounds = new int[instance.NumberFacilities];
for (int i = 0; i < instance.NumberFacilities; i++) {
lowerBounds[i] = 0;
upperBounds[i] = instance.NumberFacilities - 1;
}
DiscreteMA memetic = new DiscreteMA4QAP(instance, (int)popSize, mutProbability, lowerBounds, upperBounds);
// Solving the problem and writing the best solution found.
memetic.Run(timeLimit - (int)timePenalty);
QAPSolution solution = new QAPSolution(instance, memetic.BestIndividual);
solution.Write(fileOutput);
}
public void Start(string fileInput, string fileOutput, int timeLimit)
{
QAPInstance instance = new QAPInstance(fileInput);
// Setting the parameters of the PSO for this instance of the problem.
int[] lowerBounds = new int[instance.NumberFacilities];
int[] upperBounds = new int[instance.NumberFacilities];
for (int i = 0; i < instance.NumberFacilities; i++) {
lowerBounds[i] = 0;
upperBounds[i] = instance.NumberFacilities - 1;
}
DiscretePSO pso = new DiscretePSO2OptBest4QAP(instance, (int)particlesCount, prevConf, neighConf, lowerBounds, upperBounds);
// Solving the problem and writing the best solution found.
pso.Run(timeLimit - (int)timePenalty);
QAPSolution solution = new QAPSolution(instance, pso.BestPosition);
solution.Write(fileOutput);
}
public void Start(string fileInput, string fileOutput, int timeLimit)
{
QAPInstance instance = new QAPInstance(fileInput);
// Setting the parameters of the UMDA for this instance of the problem.
int popSize = (int) Math.Ceiling(popFactor * instance.NumberFacilities);
int[] lowerBounds = new int[instance.NumberFacilities];
int[] upperBounds = new int[instance.NumberFacilities];
for (int i = 0; i < instance.NumberFacilities; i++) {
lowerBounds[i] = 0;
upperBounds[i] = instance.NumberFacilities - 1;
}
DiscreteUMDA umda = new DiscreteUMDA2OptBest4QAP(instance, popSize, truncFactor, lowerBounds, upperBounds);
// Solving the problem and writing the best solution found.
umda.Run(timeLimit - timePenalty);
QAPSolution solution = new QAPSolution(instance, umda.BestIndividual);
solution.Write(fileOutput);
}
public void Start(string fileInput, string fileOutput, int timeLimit)
{
QAPInstance instance = new QAPInstance(fileInput);
// Setting the parameters of the PSO for this instance of the problem.
int[] lowerBounds = new int[instance.NumberFacilities];
int[] upperBounds = new int[instance.NumberFacilities];
for (int i = 0; i < instance.NumberFacilities; i++)
{
lowerBounds[i] = 0;
upperBounds[i] = instance.NumberFacilities - 1;
}
DiscretePSO pso = new DiscretePSO2OptFirst4QAP(instance, (int)particlesCount, prevConf, neighConf, lowerBounds, upperBounds);
// Solving the problem and writing the best solution found.
pso.Run(timeLimit - (int)timePenalty);
QAPSolution solution = new QAPSolution(instance, pso.BestPosition);
solution.Write(fileOutput);
}
public void Start(string fileInput, string fileOutput, int timeLimit)
{
QAPInstance instance = new QAPInstance(fileInput);
// Setting the parameters of the GA for this instance of the problem.
int[] lowerBounds = new int[instance.NumberFacilities];
int[] upperBounds = new int[instance.NumberFacilities];
for (int i = 0; i < instance.NumberFacilities; i++)
{
lowerBounds[i] = 0;
upperBounds[i] = instance.NumberFacilities - 1;
}
DiscreteGA genetic = new DiscreteGA2OptBest4QAP(instance, (int)popSize, mutProbability, lowerBounds, upperBounds);
// Solving the problem and writing the best solution found.
genetic.Run(timeLimit - (int)timePenalty);
QAPSolution solution = new QAPSolution(instance, genetic.BestIndividual);
solution.Write(fileOutput);
}
// Implementation of the 2-opt (best improvement) local search algorithm.
public static void LocalSearch2OptBest(QAPInstance instance, int[] assignment)
{
int tmp;
int firstSwapItem = 0, secondSwapItem = 0;
double currentFitness, bestFitness;
bestFitness = Fitness(instance, assignment);
for (int j = 1; j < assignment.Length; j++)
{
for (int i = 0; i < j; i++)
{
// Swap the items.
tmp = assignment[j];
assignment[j] = assignment[i];
assignment[i] = tmp;
// Evaluate the fitness of this new solution.
currentFitness = Fitness(instance, assignment);
if (currentFitness < bestFitness)
{
firstSwapItem = j;
secondSwapItem = i;
bestFitness = currentFitness;
}
// Undo the swap.
tmp = assignment[j];
assignment[j] = assignment[i];
assignment[i] = tmp;
}
}
// Use the best assignment.
if (firstSwapItem != secondSwapItem)
{
tmp = assignment[firstSwapItem];
assignment[firstSwapItem] = assignment[secondSwapItem];
assignment[secondSwapItem] = tmp;
}
}
public static int[] GetNeighbor(QAPInstance instance, int[] solution)
{
int[] neighbor = new int[instance.NumberFacilities];
int a = Statistics.RandomDiscreteUniform(0, solution.Length - 1);
int b = a;
while (b == a) {
b = Statistics.RandomDiscreteUniform(0, solution.Length - 1);
}
for (int i = 0; i < solution.Length; i++) {
if (i == a) {
neighbor[i] = solution[b];
}
else if (i == b) {
neighbor[i] = solution[a];
}
else {
neighbor[i] = solution[i];
}
}
return neighbor;
}
public void Start(string fileInput, string fileOutput, int timeLimit)
{
QAPInstance instance = new QAPInstance(fileInput);
// Setting the parameters of the UMDA for this instance of the problem.
int popSize = (int)Math.Ceiling(popFactor * instance.NumberFacilities);
int[] lowerBounds = new int[instance.NumberFacilities];
int[] upperBounds = new int[instance.NumberFacilities];
for (int i = 0; i < instance.NumberFacilities; i++)
{
lowerBounds[i] = 0;
upperBounds[i] = instance.NumberFacilities - 1;
}
DiscreteUMDA umda = new DiscreteUMDA4QAP(instance, popSize, truncFactor, lowerBounds, upperBounds);
// Solving the problem and writing the best solution found.
umda.Run(timeLimit - (int)timePenalty);
QAPSolution solution = new QAPSolution(instance, umda.BestIndividual);
solution.Write(fileOutput);
}
public QAPSolution(QAPInstance instance, int[] assignment)
{
Instance = instance;
Assignment = assignment;
}
public DiscreteGRASP2OptFirst4QAP(QAPInstance instance, double rclThreshold)
: base(rclThreshold)
{
Instance = instance;
}
public static int[] RandomSolution(QAPInstance instance)
{
int[] solution = new int[instance.NumberFacilities];
List<int> facilities = new List<int>();
for (int facility = 0; facility < instance.NumberFacilities; facility++) {
facilities.Add(facility);
}
for (int i = 0; i < instance.NumberFacilities; i++) {
int facilityIndex = Statistics.RandomDiscreteUniform(0, facilities.Count - 1);
int facility = facilities[facilityIndex];
facilities.RemoveAt(facilityIndex);
solution[i] = facility;
}
return solution;
}
// Implementation of the 2-opt (first improvement) local search algorithm.
public static void LocalSearch2OptFirst(QAPInstance instance, int[] assignment)
{
int tmp;
double currentFitness, bestFitness;
bestFitness = Fitness(instance, assignment);
for (int j = 1; j < assignment.Length; j++) {
for (int i = 0; i < j; i++) {
// Swap the items.
tmp = assignment[j];
assignment[j] = assignment[i];
assignment[i] = tmp;
// Evaluate the fitness of this new solution.
currentFitness = Fitness(instance, assignment);
if (currentFitness < bestFitness) {
return;
}
// Undo the swap.
tmp = assignment[j];
assignment[j] = assignment[i];
assignment[i] = tmp;
}
}
}
public DiscreteGRASP2OptBest4QAP( QAPInstance instance, double rclThreshold)
: base(rclThreshold)
{
Instance = instance;
}
public static void Repair(QAPInstance instance, int[] individual)
{
int facilitiesCount = 0;
bool[] facilitiesUsed = new bool[instance.NumberFacilities];
bool[] facilitiesRepeated = new bool[instance.NumberFacilities];
// Get information to decide if the individual is valid.
for (int i = 0; i < instance.NumberFacilities; i++) {
if (!facilitiesUsed[individual[i]]) {
facilitiesCount += 1;
facilitiesUsed[individual[i]] = true;
}
else {
facilitiesRepeated[i] = true;
}
}
// If the individual is invalid, make it valid.
if (facilitiesCount != instance.NumberFacilities) {
for (int i = 0; i < facilitiesRepeated.Length; i++) {
if (facilitiesRepeated[i]) {
int count = Statistics.RandomDiscreteUniform(1, instance.NumberFacilities - facilitiesCount);
for (int f = 0; f < facilitiesUsed.Length; f++) {
if (!facilitiesUsed[f]) {
count -= 1;
if (count == 0) {
individual[i] = f;
facilitiesRepeated[i] = false;
facilitiesUsed[f] = true;
facilitiesCount += 1;
break;
}
}
}
}
}
}
}
// Implementation of the GRC solution's construction algorithm.
public static int[] GRCSolution(QAPInstance instance, double rclThreshold)
{
int numFacilities = instance.NumberFacilities;
int[] assigment = new int[numFacilities];
int totalFacilities = numFacilities;
int index = 0;
double best = 0;
double cost = 0;
int facility = 0;
// Restricted Candidate List.
SortedList <double, int> rcl = new SortedList <double, int>();
// Available cities.
bool[] assigned = new bool[numFacilities];
assigment[0] = Statistics.RandomDiscreteUniform(0, numFacilities - 1);
assigned[assigment[0]] = true;
index++;
numFacilities--;
while (numFacilities > 0)
{
rcl = new SortedList <double, int>();
for (int i = 0; i < totalFacilities; i++)
{
if (!assigned[i])
{
cost = 0;
for (int j = 0; j < index; j++)
{
cost += instance.Distances[j, index] * instance.Flows[assigment[j], i];
}
if (rcl.Count == 0)
{
best = cost;
rcl.Add(cost, i);
}
else if (cost < best)
{
// The new assignment is the new best;
best = cost;
for (int j = rcl.Count - 1; j > 0; j--)
{
if (rcl.Keys[j] > rclThreshold * best)
{
rcl.RemoveAt(j);
}
else
{
break;
}
}
rcl.Add(cost, i);
}
else if (cost < rclThreshold * best)
{
// The new assigment is a mostly good candidate.
rcl.Add(cost, i);
}
}
}
facility = rcl.Values[Statistics.RandomDiscreteUniform(0, rcl.Count - 1)];
assigned[facility] = true;
assigment[index] = facility;
index++;
numFacilities--;
}
return(assigment);
}