public void Start(string inputFile, string outputFile, int timeLimit)
{
TSPInstance instance = new TSPInstance(inputFile);
DiscreteSS ss = new DiscreteSS2OptBest4TSP(instance, poolSize, refSetSize, explorationFactor);
ss.Run(timeLimit - timePenalty);
TSPSolution solution = new TSPSolution(instance, ss.BestSolution);
solution.Write(outputFile);
}
public void Start(string fileInput, string fileOutput, int timeLimit)
{
TSPInstance instance = new TSPInstance(fileInput);
int[] assignment = TSPUtils.GreedySolution(instance);
TSPUtils.LocalSearch2OptFirst(instance, assignment);
TSPSolution solution = new TSPSolution(instance, assignment);
solution.Write(fileOutput);
}
public void Start(string inputFile, string outputFile, int timeLimit)
{
TSPInstance instance = new TSPInstance(inputFile);
int levelLength = (int) Math.Ceiling(levelLengthFactor * (instance.NumberCities * (instance.NumberCities - 1)));
DiscreteHMSAwGRASP2OptBest4TSP hm = new DiscreteHMSAwGRASP2OptBest4TSP(instance, rclTreshold, graspIterations, initialSolutions, levelLength, tempReduction);
hm.Run(timeLimit - timePenalty);
TSPSolution solution = new TSPSolution(instance, hm.BestSolution);
solution.Write(outputFile);
}
public void Start(string fileInput, string fileOutput, int timeLimit)
{
TSPInstance instance = new TSPInstance(fileInput);
int levelLength = (int) Math.Ceiling(levelLengthFactor * (instance.NumberCities * (instance.NumberCities - 1)));
DiscreteSA sa = new DiscreteSA4TSP(instance, initialSolutions, levelLength, tempReduction);
sa.Run(timeLimit - timePenalty);
TSPSolution solution = new TSPSolution(instance, sa.BestSolution);
solution.Write(fileOutput);
}
public void Start(string inputFile, string outputFile, int timeLimit)
{
TSPInstance instance = new TSPInstance(inputFile);
MaxMinAntSystem aco = new MaxMinAntSystem2OptBest4TSP(instance, numberAnts, rho, alpha, beta, maxReinit, candidateLength, candidateWeight);
// Solving the problem and writing the best solution found.
aco.Run(timeLimit - timePenalty);
TSPSolution solution = new TSPSolution(instance, aco.BestSolution);
solution.Write(outputFile);
}
public void Start(string inputFile, string outputFile, int timeLimit)
{
TSPInstance instance = new TSPInstance(inputFile);
int neighborChecks = (int) Math.Ceiling(neighborChecksFactor * (instance.NumberCities * (instance.NumberCities - 1)));
int tabuListLength = (int) Math.Ceiling(tabuListFactor * instance.NumberCities);
DiscreteTS ts = new DiscreteTS4TSP(instance, rclTreshold, tabuListLength, neighborChecks);
ts.Run(timeLimit - timePenalty);
TSPSolution solution = new TSPSolution(instance, ts.BestSolution);
solution.Write(outputFile);
}
public void Start(string inputFile, string outputFile, int timeLimit)
{
TSPInstance instance = new TSPInstance(inputFile);
DiscreteSS ss = new DiscreteSS4TSP(instance, poolSize, refSetSize, explorationFactor);
ss.Run(timeLimit - timePenalty);
TSPSolution solution = new TSPSolution(instance, ss.BestSolution);
solution.Write(outputFile);
}
public void Start(string fileInput, string fileOutput, int timeLimit)
{
TSPInstance instance = new TSPInstance(fileInput);
int[] assignment = TSPUtils.GreedySolution(instance);
TSPUtils.LocalSearch2OptFirst(instance, assignment);
TSPSolution solution = new TSPSolution(instance, assignment);
solution.Write(fileOutput);
}
public void Start(string inputFile, string outputFile, int timeLimit)
{
TSPInstance instance = new TSPInstance(inputFile);
int levelLength = (int)Math.Ceiling(levelLengthFactor * (instance.NumberCities * (instance.NumberCities - 1)));
DiscreteHMSAwGRASP2OptFirst4TSP hm = new DiscreteHMSAwGRASP2OptFirst4TSP(instance, rclTreshold, graspIterations, initialSolutions, levelLength, tempReduction);
hm.Run(timeLimit - timePenalty);
TSPSolution solution = new TSPSolution(instance, hm.BestSolution);
solution.Write(outputFile);
}
public void Start(string fileInput, string fileOutput, int timeLimit)
{
TSPInstance instance = new TSPInstance(fileInput);
int levelLength = (int)Math.Ceiling(levelLengthFactor * (instance.NumberCities * (instance.NumberCities - 1)));
DiscreteSA sa = new DiscreteSA4TSP(instance, initialSolutions, levelLength, tempReduction);
sa.Run(timeLimit - timePenalty);
TSPSolution solution = new TSPSolution(instance, sa.BestSolution);
solution.Write(fileOutput);
}
public void Start(string inputFile, string outputFile, int timeLimit)
{
TSPInstance instance = new TSPInstance(inputFile);
MaxMinAntSystem aco = new MaxMinAntSystem2OptBest4TSP(instance, numberAnts, rho, alpha, beta, maxReinit, candidateLength, candidateWeight);
// Solving the problem and writing the best solution found.
aco.Run(timeLimit - timePenalty);
TSPSolution solution = new TSPSolution(instance, aco.BestSolution);
solution.Write(outputFile);
}
public void Start(string fileInput, string fileOutput, int timeLimit)
{
TSPInstance instance = new TSPInstance(fileInput);
// Setting the parameters of the GRASP for this instance of the problem.
DiscreteGRASP grasp = new DiscreteGRASP2OptBest4TSP(instance, rclThreshold);
// Solving the problem and writing the best solution found.
grasp.Run(timeLimit - (int)timePenalty, RunType.TimeLimit);
TSPSolution solution = new TSPSolution(instance, grasp.BestSolution);
solution.Write(fileOutput);
}
public void Start(string inputFile, string outputFile, int timeLimit)
{
TSPInstance instance = new TSPInstance(inputFile);
int neighborChecks = (int)Math.Ceiling(neighborChecksFactor * (instance.NumberCities * (instance.NumberCities - 1)));
int tabuListLength = (int)Math.Ceiling(tabuListFactor * instance.NumberCities);
DiscreteTS ts = new DiscreteTS4TSP(instance, rclTreshold, tabuListLength, neighborChecks);
ts.Run(timeLimit - timePenalty);
TSPSolution solution = new TSPSolution(instance, ts.BestSolution);
solution.Write(outputFile);
}
public void Start(string fileInput, string fileOutput, int timeLimit)
{
TSPInstance instance = new TSPInstance(fileInput);
// Setting the parameters of the GRASP for this instance of the problem.
DiscreteGRASP grasp = new DiscreteGRASP2OptFirst4TSP(instance, rclThreshold);
// Solving the problem and writing the best solution found.
grasp.Run(timeLimit - (int)timePenalty, RunType.TimeLimit);
TSPSolution solution = new TSPSolution(instance, grasp.BestSolution);
solution.Write(fileOutput);
}
public void Start(string inputFile, string outputFile, int timeLimit)
{
TSPInstance instance = new TSPInstance(inputFile);
int[] lowerBounds = new int[instance.NumberCities];
int[] upperBounds = new int[instance.NumberCities];
for (int i = 0; i < instance.NumberCities; i++) {
lowerBounds[i] = 0;
upperBounds[i] = instance.NumberCities - 1;
}
DiscreteILS ils = new DiscreteILS2OptFirst4TSP(instance, restartIterations, lowerBounds, upperBounds);
ils.Run(timeLimit - timePenalty);
TSPSolution solution = new TSPSolution(instance, ils.BestSolution);
solution.Write(outputFile);
}
public void Start(string fileInput, string fileOutput, int timeLimit)
{
TSPInstance instance = new TSPInstance(fileInput);
// Setting the parameters of the PSO for this instance of the problem.
int[] lowerBounds = new int[instance.NumberCities];
int[] upperBounds = new int[instance.NumberCities];
for (int i = 0; i < instance.NumberCities; i++) {
lowerBounds[i] = 0;
upperBounds[i] = instance.NumberCities - 1;
}
DiscretePSO pso = new DiscretePSO4TSP(instance, (int)particlesCount, prevConf, neighConf, lowerBounds, upperBounds);
// Solving the problem and writing the best solution found.
pso.Run(timeLimit - (int)timePenalty);
TSPSolution solution = new TSPSolution(instance, pso.BestPosition);
solution.Write(fileOutput);
}
public void Start(string fileInput, string fileOutput, int timeLimit)
{
TSPInstance instance = new TSPInstance(fileInput);
// Setting the parameters of the MA for this instance of the problem.
int[] lowerBounds = new int[instance.NumberCities];
int[] upperBounds = new int[instance.NumberCities];
for (int i = 0; i < instance.NumberCities; i++) {
lowerBounds[i] = 0;
upperBounds[i] = instance.NumberCities - 1;
}
DiscreteMA memetic = new DiscreteMA4TSP(instance, (int)popSize, mutProbability, lowerBounds, upperBounds);
// Solving the problem and writing the best solution found.
memetic.Run(timeLimit - (int)timePenalty);
TSPSolution solution = new TSPSolution(instance, memetic.BestIndividual);
solution.Write(fileOutput);
}
public void Start(string inputFile, string outputFile, int timeLimit)
{
TSPInstance instance = new TSPInstance(inputFile);
int[] lowerBounds = new int[instance.NumberCities];
int[] upperBounds = new int[instance.NumberCities];
for (int i = 0; i < instance.NumberCities; i++)
{
lowerBounds[i] = 0;
upperBounds[i] = instance.NumberCities - 1;
}
DiscreteILS ils = new DiscreteILS2OptBest4TSP(instance, restartIterations, lowerBounds, upperBounds);
ils.Run(timeLimit - timePenalty);
TSPSolution solution = new TSPSolution(instance, ils.BestSolution);
solution.Write(outputFile);
}
public void Start(string fileInput, string fileOutput, int timeLimit)
{
TSPInstance instance = new TSPInstance(fileInput);
// Setting the parameters of the UMDA for this instance of the problem.
int popSize = (int) Math.Ceiling(popFactor * instance.NumberCities);
int[] lowerBounds = new int[instance.NumberCities];
int[] upperBounds = new int[instance.NumberCities];
for (int i = 0; i < instance.NumberCities; i++) {
lowerBounds[i] = 0;
upperBounds[i] = instance.NumberCities - 1;
}
DiscreteUMDA umda = new DiscreteUMDA2OptFirst4TSP(instance, popSize, truncFactor, lowerBounds, upperBounds);
// Solving the problem and writing the best solution found.
umda.Run(timeLimit - (int)timePenalty);
TSPSolution solution = new TSPSolution(instance, umda.BestIndividual);
solution.Write(fileOutput);
}
public void Start(string fileInput, string fileOutput, int timeLimit)
{
TSPInstance instance = new TSPInstance(fileInput);
// Setting the parameters of the GA for this instance of the problem.
int[] lowerBounds = new int[instance.NumberCities];
int[] upperBounds = new int[instance.NumberCities];
for (int i = 0; i < instance.NumberCities; i++)
{
lowerBounds[i] = 0;
upperBounds[i] = instance.NumberCities - 1;
}
DiscreteGA genetic = new DiscreteGA2OptFirst4TSP(instance, (int)popSize, mutProbability, lowerBounds, upperBounds);
// Solving the problem and writing the best solution found.
genetic.Run(timeLimit - (int)timePenalty);
TSPSolution solution = new TSPSolution(instance, genetic.BestIndividual);
solution.Write(fileOutput);
}
public void Start(string fileInput, string fileOutput, int timeLimit)
{
TSPInstance instance = new TSPInstance(fileInput);
// Setting the parameters of the PSO for this instance of the problem.
int[] lowerBounds = new int[instance.NumberCities];
int[] upperBounds = new int[instance.NumberCities];
for (int i = 0; i < instance.NumberCities; i++)
{
lowerBounds[i] = 0;
upperBounds[i] = instance.NumberCities - 1;
}
DiscretePSO pso = new DiscretePSO2OptFirst4TSP(instance, (int)particlesCount, prevConf, neighConf, lowerBounds, upperBounds);
// Solving the problem and writing the best solution found.
pso.Run(timeLimit - (int)timePenalty);
TSPSolution solution = new TSPSolution(instance, pso.BestPosition);
solution.Write(fileOutput);
}
public void Start(string fileInput, string fileOutput, int timeLimit)
{
TSPInstance instance = new TSPInstance(fileInput);
// Setting the parameters of the UMDA for this instance of the problem.
int popSize = (int)Math.Ceiling(popFactor * instance.NumberCities);
int[] lowerBounds = new int[instance.NumberCities];
int[] upperBounds = new int[instance.NumberCities];
for (int i = 0; i < instance.NumberCities; i++)
{
lowerBounds[i] = 0;
upperBounds[i] = instance.NumberCities - 1;
}
DiscreteUMDA umda = new DiscreteUMDA2OptBest4TSP(instance, popSize, truncFactor, lowerBounds, upperBounds);
// Solving the problem and writing the best solution found.
umda.Run(timeLimit - timePenalty);
TSPSolution solution = new TSPSolution(instance, umda.BestIndividual);
solution.Write(fileOutput);
}