int main(int argc, char *argv[])
{
GainType Cost;
double Time, LastTime = GetTime();
/* Read the specification of the problem */
if (argc >= 2)
ParameterFileName = argv[1];
ReadParameters();
MaxMatrixDimension = 10000;
ReadProblem();
if (SubproblemSize > 0) {
if (DelaunayPartitioning)
SolveDelaunaySubproblems();
else if (KarpPartitioning)
SolveKarpSubproblems();
else if (KCenterPartitioning)
SolveKCenterSubproblems();
else if (KMeansPartitioning)
SolveKMeansSubproblems();
else if (RohePartitioning)
SolveRoheSubproblems();
else if (MoorePartitioning || SierpinskiPartitioning)
SolveSFCSubproblems();
else
SolveTourSegmentSubproblems();
return EXIT_SUCCESS;
}
AllocateStructures();
CreateCandidateSet();
InitializeStatistics();
if (Norm != 0)
BestCost = PLUS_INFINITY;
else {
/* The ascent has solved the problem! */
Optimum = BestCost = (GainType) LowerBound;
UpdateStatistics(Optimum, GetTime() - LastTime);
RecordBetterTour();
RecordBestTour();
WriteTour(OutputTourFileName, BestTour, BestCost);
WriteTour(TourFileName, BestTour, BestCost);
Runs = 0;
}
/* Find a specified number (Runs) of local optima */
for (Run = 1; Run <= Runs; Run++) {
LastTime = GetTime();
Cost = FindTour(); /* using the Lin-Kernighan heuristic */
if (MaxPopulationSize > 1) {
/* Genetic algorithm */
int i;
for (i = 0; i < PopulationSize; i++) {
GainType OldCost = Cost;
Cost = MergeTourWithIndividual(i);
if (TraceLevel >= 1 && Cost < OldCost) {
printff(" Merged with %d: Cost = " GainFormat, i + 1,
Cost);
if (Optimum != MINUS_INFINITY && Optimum != 0)
printff(", Gap = %0.4f%%",
100.0 * (Cost - Optimum) / Optimum);
printff("\n");
}
}
if (!HasFitness(Cost)) {
if (PopulationSize < MaxPopulationSize) {
AddToPopulation(Cost);
if (TraceLevel >= 1)
PrintPopulation();
} else if (Cost < Fitness[PopulationSize - 1]) {
i = ReplacementIndividual(Cost);
ReplaceIndividualWithTour(i, Cost);
if (TraceLevel >= 1)
PrintPopulation();
}
}
} else if (Run > 1)
Cost = MergeBetterTourWithBestTour();
if (Cost < BestCost) {
BestCost = Cost;
RecordBetterTour();
RecordBestTour();
WriteTour(OutputTourFileName, BestTour, BestCost);
WriteTour(TourFileName, BestTour, BestCost);
}
if (Cost < Optimum) {
if (FirstNode->InputSuc) {
Node *N = FirstNode;
while ((N = N->InputSuc = N->Suc) != FirstNode);
}
Optimum = Cost;
printff("*** New optimum = " GainFormat " ***\n\n", Optimum);
}
Time = fabs(GetTime() - LastTime);
UpdateStatistics(Cost, Time);
if (TraceLevel >= 1 && Cost != PLUS_INFINITY) {
printff("Run %d: Cost = " GainFormat, Run, Cost);
if (Optimum != MINUS_INFINITY && Optimum != 0)
printff(", Gap = %0.4f%%",
100.0 * (Cost - Optimum) / Optimum);
printff(", Time = %0.2f sec. %s\n\n", Time,
Cost < Optimum ? "<" : Cost == Optimum ? "=" : "");
}
if (PopulationSize >= 2 &&
(PopulationSize == MaxPopulationSize ||
Run >= 2 * MaxPopulationSize) && Run < Runs) {
Node *N;
int Parent1, Parent2;
Parent1 = LinearSelection(PopulationSize, 1.25);
do
Parent2 = LinearSelection(PopulationSize, 1.25);
while (Parent2 == Parent1);
ApplyCrossover(Parent1, Parent2);
N = FirstNode;
do {
int d = C(N, N->Suc);
AddCandidate(N, N->Suc, d, INT_MAX);
AddCandidate(N->Suc, N, d, INT_MAX);
N = N->InitialSuc = N->Suc;
}
while (N != FirstNode);
}
SRandom(++Seed);
}
PrintStatistics();
return EXIT_SUCCESS;
}
ReturnFlag LKH::LKHAlg::run_()
{
GainType Cost, OldOptimum;
double Time, LastTime = GetTime();
if (SubproblemSize > 0) {
if (DelaunayPartitioning)
SolveDelaunaySubproblems();
else if (KarpPartitioning)
SolveKarpSubproblems();
else if (KCenterPartitioning)
SolveKCenterSubproblems();
else if (KMeansPartitioning)
SolveKMeansSubproblems();
else if (RohePartitioning)
SolveRoheSubproblems();
else if (MoorePartitioning || SierpinskiPartitioning)
SolveSFCSubproblems();
else
SolveTourSegmentSubproblems();
}
AllocateStructures();
CreateCandidateSet();
InitializeStatistics();
if (Norm != 0)
BestCost = PLUS_INFINITY;
else {
/* The ascent has solved the problem! */
Optimum = BestCost = (GainType) LowerBound;
UpdateStatistics(Optimum, GetTime() - LastTime);
RecordBetterTour();
RecordBestTour();
WriteTour(OutputTourFileName, BestTour, BestCost);
WriteTour(TourFileName, BestTour, BestCost);
Runs = 0;
}
/* Find a specified number (Runs) of local optima */
for (Run = 1; Run <= Runs; Run++) {
LastTime = GetTime();
Cost = FindTour(); /* using the Lin-Kernighan heuristic */
if (*MaxPopulationSize > 1) {
/* Genetic algorithm */
int i;
for (i = 0; i < *PopulationSize; i++) {
GainType OldCost = Cost;
Cost = MergeTourWithIndividual(i,this);
if (TraceLevel >= 1 && Cost < OldCost) {
printff(" Merged with %d: Cost = " GainFormat, i + 1,
Cost);
if (Optimum != MINUS_INFINITY && Optimum != 0)
printff(", Gap = %0.4f%%",
100.0 * (Cost - Optimum) / Optimum);
printff("\n");
}
}
if (!HasFitness(Cost)) {
if (*PopulationSize < *MaxPopulationSize) {
AddToPopulation(Cost,this);
if (TraceLevel >= 1)
PrintPopulation(this);
} else if (Cost < Fitness.get()[*PopulationSize - 1]) {
i = ReplacementIndividual(Cost,this);
ReplaceIndividualWithTour(i, Cost,this);
if (TraceLevel >= 1)
PrintPopulation(this);
}
}
} else if (Run > 1)
Cost = MergeBetterTourWithBestTour();
if (Cost < BestCost) {
BestCost = Cost;
RecordBetterTour();
RecordBestTour();
WriteTour(OutputTourFileName, BestTour, BestCost);
WriteTour(TourFileName, BestTour, BestCost);
}
OldOptimum = Optimum;
if (Cost < Optimum) {
if (FirstNode->InputSuc) {
Node *N = FirstNode;
while ((N = N->InputSuc = N->Suc) != FirstNode);
}
Optimum = Cost;
printff("*** New optimum = " GainFormat " ***\n\n", Optimum);
}
Time = fabs(GetTime() - LastTime);
UpdateStatistics(Cost, Time);
if (TraceLevel >= 1 && Cost != PLUS_INFINITY) {
printff("Run %d: Cost = " GainFormat, Global::msp_global->m_runId, Cost);
if (Optimum != MINUS_INFINITY && Optimum != 0)
printff(", Gap = %0.4f%%",
100.0 * (Cost - Optimum) / Optimum);
// printff(", Time = %0.2f sec. %s\n\n", Time,
// Cost < Optimum ? "<" : Cost == Optimum ? "=" : "");
printff(", Time = %0.2f sec. \n", Time);
}
if (StopAtOptimum && Cost == OldOptimum && *MaxPopulationSize >= 1) {
Runs = Run;
break;
}
if (*PopulationSize >= 2 &&
(*PopulationSize == *MaxPopulationSize ||
Run >= 2 * *MaxPopulationSize) && Run < Runs) {
Node *N;
int Parent1, Parent2;
Parent1 = LinearSelection(*PopulationSize, 1.25,this);
do
Parent2 = LinearSelection(*PopulationSize, 1.25,this);
while (Parent2 == Parent1);
ApplyCrossover(Parent1, Parent2,this);
N = FirstNode;
do {
int d = (this->*C)(N, N->Suc);
AddCandidate(N, N->Suc, d, INT_MAX);
AddCandidate(N->Suc, N, d, INT_MAX);
N = N->InitialSuc = N->Suc;
}
while (N != FirstNode);
}
mv_cost[Global::msp_global->m_runId]=Cost;
SRandom(++Seed);
}
// PrintStatistics();
freeAll();
FreeStructures();
return Return_Terminate;
}
