Result *TSPSimulatedAnnealing::solve()
{
/**
* 1). losujesz rozwiazanie
* 2). losujesz sąsiada żeby do niego przejść
* 3). przechodzisz jak jest lepszy a jak nie to sprawdzasz prawdopodobieństwo przejścia
* 4). koniec algorytmu, gdy:
* - osiągnie daną liczbę kroków
* - jeżeli nie ma poprawy po określonej liczbie kroków
* - przy przejśiu sprawdzamy czy temperatuda nie opadła poniżej minimum
*/
Result *cur;
Result *next;
Result *best = NULL;
u_int32_t bestDistance = 0, noBadMovesCounter = 0;
u_int32_t loopCounter = 0;
u_int32_t neighorsVisited = 0, betterSolutionsCount = 0;
u_int32_t nextDistance, curDistance, worseSolutions = 0;
//printf("%f %f %f %d\n", _startTemperature, _endTemperature, _alpha, _maxBadMoves);
_temperature = _startTemperature;
cur = generateRandomResult();
// TSPGreedySolver2 *greedySolver2;
// greedySolver2 = new TSPGreedySolver2(_instance, "");
// cur = greedySolver2->solve();
// delete greedySolver2;
best = new Result(*cur);
bestDistance = calculateDistance(cur);
while ((_temperature > _endTemperature) &&
(loopCounter < getNumberOfSteps()) && (noBadMovesCounter < _maxBadMoves))
{
next = new Result(*cur);
next->swapRandomly();
nextDistance = calculateDistance(next);
next->setCalculatedDistance(nextDistance);
neighorsVisited++;
float lotery = ((float) (rand() % RAND_MAX) / (float) RAND_MAX);
float accProb = acceptanceProbability(curDistance, nextDistance);
//kiedy przechodzimy do nowego rozwiazania
if ((nextDistance < curDistance) || (lotery > accProb))
{
delete cur;
cur = next;
noBadMovesCounter = 0;
if (nextDistance >= curDistance)
{
//printf("worse %f %f %f\n", lotery, accProb, _temperature);
worseSolutions++;
}
if (nextDistance < bestDistance)
{
if (best != NULL)
delete best;
betterSolutionsCount++;
best = new Result(*next);
bestDistance = nextDistance;
best->setCalculatedDistance(bestDistance);
}
curDistance = calculateDistance(cur);
cur->setCalculatedDistance(curDistance);
}
else
{
noBadMovesCounter++;
delete next;
}
loopCounter++;
_temperature *= _alpha;
}
// printf("worse: %d\n", worseSolutions);
// printf("temperature: %f\n", _temperature);
// printf("loopCounter: %d\n", loopCounter);
// printf("bad moves: %d\n", noBadMovesCounter);
best->setNeighborsVisited(neighorsVisited);
best->setStepsCount(loopCounter);
best->setBetterSolutionsCount(betterSolutionsCount);
return best;
}
Data MatrixGraph::simulatedAnnealing(uint temperature)
{
clock_t overallTime = clock();
initRand();
stringstream results;
vector<uint> route, bestRoute;
route.reserve(vertexNumber);
bestRoute.reserve(vertexNumber);
uint prevCost = greedyAlg(route), bestCost;
route.pop_back();
bestRoute = route;
bestCost = prevCost;
if (!temperature)
temperature = vertexNumber << 10;
default_random_engine gen(uint(time(nullptr)));
uniform_real_distribution<double> doubleRnd(0.0, 1.0);
uint i = 0;
for (; temperature; --temperature, ++i)
{
i %= route.size();
uint firstIndex = rand() % (route.size());
uint secondIndex = rand() % (route.size() - 1);
if (secondIndex >= firstIndex)
++secondIndex;
vector<uint> tmpVector(route);
uint tmp = tmpVector[firstIndex];
tmpVector[firstIndex] = tmpVector[secondIndex];
tmpVector[secondIndex] = tmp;
uint tmpCost = calculateCost(tmpVector);
if (tmpCost < prevCost)
{
route = tmpVector;
prevCost = tmpCost;
}
else
{
if (acceptanceProbability(prevCost, tmpCost, temperature) >= doubleRnd(gen))
{
route = tmpVector;
prevCost = tmpCost;
}
}
// Śledzenie najlepszego rozwiązania
if (prevCost < bestCost)
{
bestRoute = route;
bestCost = prevCost;
}
}
double duration = (clock() - overallTime) / (double)CLOCKS_PER_SEC;
results << "Koszt drogi: " << bestCost << "\nCalkowity czas trwania: " << duration << " sekund\n";
return Data(bestRoute, bestCost, results.str(), duration);
}
