void runLocalSearch(FILE *fileResults, HistoricalData *fileReader, int populationSize, int numberOfSteps, int cycle)
{
printf ("LS Bugs %d - Cycle %d \r", fileReader->openBugList.size(), cycle);
int maxEvaluations = (int) (numberOfSteps * populationSize * (1 - TOPMOST_PERCENTIL));
time_t start = time (NULL);
LocalSearch *method = new LocalSearch(fileReader, maxEvaluations);
Chromosome *bestSolution = method->run();
time_t finish = time (NULL);
int diff = (int) (finish - start);
printSchedule(bestSolution, "LS", cycle, populationSize, numberOfSteps, fileReader->openBugList.size());
fprintf(fileResults, "Cycle %d; Time = %d; Cost = %.0f; Makespan = %d\n", cycle, diff, bestSolution->getCost(), bestSolution->getSchedule()->calculateMakespan());
fflush(fileResults);
}
Chromosome *LocalSearch::run ()
{
// Creates a random chromossome
std::vector <double> genes;
for (unsigned k = 0; k < historicalData->openBugList.size(); k++)
genes.push_back (rand_uniform());
// Calculate cost and chromossome size
Chromosome *chromosome = new Chromosome (historicalData, genes);
double costReference = chromosome->getCost();
int evaluations = 1;
// Hill Climbing local search
int size = chromosome->genes.size();
Chromosome *neighbour = chromosome->clone();
while (evaluations <= maxEvaluations)
{
int i = 0, j;
while (i < size && evaluations <= maxEvaluations)
{
for (j = i + 1; j < size && evaluations <= maxEvaluations; j++)
{
neighbour->swapGene(i, j);
neighbour->recalculateSchedule();
evaluations++;
if (neighbour->getCost() < costReference)
{
delete chromosome;
chromosome = neighbour;
costReference = neighbour->getCost();
neighbour = neighbour->clone();
break;
}
else
neighbour->swapGene (j, i);
}
i = j;
}
// Creates new random chromossome
genes.clear();
for (unsigned k = 0; k < historicalData->openBugList.size(); k++)
genes.push_back (rand_uniform());
// Calculate cost and chromossome size
delete neighbour;
neighbour = new Chromosome (historicalData, genes);
evaluations++;
if (neighbour->getCost() < costReference)
{
delete chromosome;
chromosome = neighbour;
costReference = neighbour->getCost();
neighbour = neighbour->clone();
}
}
return chromosome;
}
void runGeneticAlgorithm(FILE *fileResults, HistoricalData *fileReader, int populationSize, int numberOfSteps, int cycle)
{
time_t start = time (NULL);
RandomKeyMethod *method = new RandomKeyMethod (fileReader, populationSize);
for (int i=0; i < numberOfSteps; i++)
{
printf ("GA Bugs %d - Cycle %d - Step %d \r", fileReader->openBugList.size(), cycle, i);
method->reproduct ();
}
Chromosome *bestSolution = method->getBestSolution();
time_t finish = time (NULL);
int diff = (int) (finish - start);
printSchedule(bestSolution, "GA", cycle, populationSize, numberOfSteps, fileReader->openBugList.size());
fprintf(fileResults, "Cycle %d; Time = %d; Cost = %.0f; Makespan = %d\n", cycle, diff, bestSolution->getCost(), bestSolution->getSchedule()->calculateMakespan());
fflush(fileResults);
}
