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);
}
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);
}
