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;
}
void ChooseInitialTour()
{
Node *N, *NextN, *FirstAlternative, *Last;
Candidate *NN;
int Alternatives, Count, i;
if (KickType > 0 && Kicks > 0 && Trial > 1) {
for (Last = FirstNode; (N = Last->BestSuc) != FirstNode; Last = N)
Follow(N, Last);
for (i = 1; i <= Kicks; i++)
KSwapKick(KickType);
return;
}
if (Trial == 1 && (!FirstNode->InitialSuc || InitialTourFraction < 1)) {
if (InitialTourAlgorithm == BORUVKA ||
InitialTourAlgorithm == GREEDY ||
InitialTourAlgorithm == MOORE ||
InitialTourAlgorithm == NEAREST_NEIGHBOR ||
InitialTourAlgorithm == QUICK_BORUVKA ||
InitialTourAlgorithm == SIERPINSKI) {
GainType Cost = InitialTourAlgorithm == MOORE ||
InitialTourAlgorithm == SIERPINSKI ?
SFCTour(InitialTourAlgorithm) : GreedyTour();
if (MaxTrials == 0) {
BetterCost = Cost;
RecordBetterTour();
}
if (!FirstNode->InitialSuc)
return;
}
}
Start:
/* Mark all nodes as "not chosen" by setting their V field to zero */
N = FirstNode;
do
N->V = 0;
while ((N = N->Suc) != FirstNode);
Count = 0;
/* Choose FirstNode without two incident fixed or common candidate edges */
do {
if (FixedOrCommonCandidates(N) < 2)
break;
}
while ((N = N->Suc) != FirstNode);
if (ProblemType == ATSP && N->Id <= DimensionSaved)
N += DimensionSaved;
FirstNode = N;
/* Move nodes with two incident fixed or common candidate edges in
front of FirstNode */
for (Last = FirstNode->Pred; N != Last; N = NextN) {
NextN = N->Suc;
if (FixedOrCommonCandidates(N) == 2)
Follow(N, Last);
}
/* Mark FirstNode as chosen */
FirstNode->V = 1;
N = FirstNode;
/* Loop as long as not all nodes have been chosen */
while (N->Suc != FirstNode) {
FirstAlternative = 0;
Alternatives = 0;
Count++;
/* Case A */
for (NN = N->CandidateSet; (NextN = NN->To); NN++) {
if (!NextN->V && Fixed(N, NextN)) {
Alternatives++;
NextN->Next = FirstAlternative;
FirstAlternative = NextN;
}
}
if (Alternatives == 0 && MergeTourFiles > 1) {
for (NN = N->CandidateSet; (NextN = NN->To); NN++) {
if (!NextN->V && IsCommonEdge(N, NextN)) {
Alternatives++;
NextN->Next = FirstAlternative;
FirstAlternative = NextN;
}
}
}
if (Alternatives == 0 && FirstNode->InitialSuc && Trial == 1 &&
Count <= InitialTourFraction * Dimension) {
/* Case B */
for (NN = N->CandidateSet; (NextN = NN->To); NN++) {
if (!NextN->V && InInitialTour(N, NextN)) {
Alternatives++;
NextN->Next = FirstAlternative;
FirstAlternative = NextN;
}
}
}
if (Alternatives == 0 && Trial > 1 &&
ProblemType != HCP && ProblemType != HPP) {
/* Case C */
for (NN = N->CandidateSet; (NextN = NN->To); NN++) {
if (!NextN->V && FixedOrCommonCandidates(NextN) < 2 &&
NN->Alpha == 0 && (InBestTour(N, NextN) ||
InNextBestTour(N, NextN))) {
Alternatives++;
NextN->Next = FirstAlternative;
FirstAlternative = NextN;
}
}
}
if (Alternatives == 0) {
/* Case D */
for (NN = N->CandidateSet; (NextN = NN->To); NN++) {
if (!NextN->V && FixedOrCommonCandidates(NextN) < 2) {
Alternatives++;
NextN->Next = FirstAlternative;
FirstAlternative = NextN;
}
}
}
if (Alternatives == 0) {
/* Case E (actually not really a random choice) */
NextN = N->Suc;
while ((FixedOrCommonCandidates(NextN) == 2 || Forbidden(N, NextN))
&& NextN->Suc != FirstNode)
NextN = NextN->Suc;
if (FixedOrCommonCandidates(NextN) == 2 || Forbidden(N, NextN)) {
FirstNode = FirstNode->Suc;
goto Start;
}
} else {
NextN = FirstAlternative;
if (Alternatives > 1) {
/* Select NextN at random among the alternatives */
i = Random() % Alternatives;
while (i--)
NextN = NextN->Next;
}
}
/* Include NextN as the successor of N */
Follow(NextN, N);
N = NextN;
N->V = 1;
}
if (Forbidden(N, N->Suc)) {
FirstNode = FirstNode->Suc;
goto Start;
}
if (MaxTrials == 0) {
GainType Cost = 0;
N = FirstNode;
do
Cost += C(N, N->Suc) - N->Pi - N->Suc->Pi;
while ((N = N->Suc) != FirstNode);
Cost /= Precision;
if (Cost < BetterCost) {
BetterCost = Cost;
RecordBetterTour();
}
}
}
GainType LKH::LKHAlg::FindTour()
{
GainType Cost;
Node *t;
int i;
double EntryTime = GetTime();
if(!OrdinalTourCost.get())
OrdinalTourCost.reset(new GainType(0));
t = FirstNode;
do
t->OldPred = t->OldSuc = t->NextBestSuc = t->BestSuc = 0;
while ((t = t->Suc) != FirstNode);
if (Run == 1 && Dimension == DimensionSaved) {
*OrdinalTourCost = 0;
for (i = 1; i < Dimension; i++)
*OrdinalTourCost += (this->*C)(&NodeSet[i], &NodeSet[i + 1])
- NodeSet[i].Pi - NodeSet[i + 1].Pi;
*OrdinalTourCost += (this->*C)(&NodeSet[Dimension], &NodeSet[1])
- NodeSet[Dimension].Pi - NodeSet[1].Pi;
*OrdinalTourCost /= Precision;
}
BetterCost = PLUS_INFINITY;
if (MaxTrials > 0)
HashInitialize(HTable);
else {
Trial = 1;
ChooseInitialTour();
}
for (Trial = 1; Trial <= MaxTrials; Trial++) {
if (GetTime() - EntryTime >= TimeLimit) {
if (TraceLevel >= 1)
printff("*** Time limit exceeded ***\n");
break;
}
/* Choose FirstNode at random */
if (Dimension == DimensionSaved)
FirstNode = &NodeSet[1 + Random() % Dimension];
else
for (i = Random() % Dimension; i > 0; i--)
FirstNode = FirstNode->Suc;
ChooseInitialTour();
Cost = LinKernighan();
if (FirstNode->BestSuc) {
/* Merge tour with current best tour */
t = FirstNode;
while ((t = t->Next = t->BestSuc) != FirstNode);
Cost = MergeWithTour();
}
if (Dimension == DimensionSaved && Cost >= *OrdinalTourCost &&
BetterCost > *OrdinalTourCost) {
/* Merge tour with ordinal tour */
for (i = 1; i < Dimension; i++)
NodeSet[i].Next = &NodeSet[i + 1];
NodeSet[Dimension].Next = &NodeSet[1];
Cost = MergeWithTour();
}
if (Cost < BetterCost) {
if (TraceLevel >= 1) {
/* printff("* %d: Cost = " GainFormat, Trial, Cost);
if (Optimum != MINUS_INFINITY && Optimum != 0)
printff(", Gap = %0.4f%%",
100.0 * (Cost - Optimum) / Optimum);
printff(", Time = %0.2f sec. %s\n",
fabs(GetTime() - EntryTime),
Cost < Optimum ? "<" : Cost == Optimum ? "=" : ""); */
}
BetterCost = Cost;
RecordBetterTour();
if (Dimension == DimensionSaved && BetterCost < BestCost)
WriteTour(OutputTourFileName, BetterTour, BetterCost);
if (StopAtOptimum && BetterCost == Optimum)
break;
AdjustCandidateSet();
HashInitialize(HTable);
HashInsert(HTable, Hash, Cost);
} else if (TraceLevel >= 2)
printff(" %d: Cost = " GainFormat ", Time = %0.2f sec.\n",
Trial, Cost, fabs(GetTime() - EntryTime));
/* Record backbones if wanted */
if (Trial <= BackboneTrials && BackboneTrials < MaxTrials) {
SwapCandidateSets(this);
AdjustCandidateSet();
if (Trial == BackboneTrials) {
if (TraceLevel >= 1) {
printff("# %d: Backbone candidates ->\n", Trial);
CandidateReport();
}
} else
SwapCandidateSets(this);
}
}
if (BackboneTrials > 0 && BackboneTrials < MaxTrials) {
if (Trial > BackboneTrials ||
(Trial == BackboneTrials &&
(!StopAtOptimum || BetterCost != Optimum)))
SwapCandidateSets(this);
t = FirstNode;
do {
free(t->BackboneCandidateSet);
t->BackboneCandidateSet = 0;
} while ((t = t->Suc) != FirstNode);
}
t = FirstNode;
if (Norm == 0) {
do
t = t->BestSuc = t->Suc;
while (t != FirstNode);
}
do
(t->Suc = t->BestSuc)->Pred = t;
while ((t = t->BestSuc) != FirstNode);
if (Trial > MaxTrials)
Trial = MaxTrials;
ResetCandidateSet();
return BetterCost;
}
int tsp_lkh()
{
GainType Cost, OldOptimum;
double Time, LastTime = GetTime();
/* Read the specification of the problem */
ReadParameters();
MaxMatrixDimension = 10000;
init();
AllocateStructures();
CreateCandidateSet();
InitializeStatistics();
BestCost = PLUS_INFINITY;
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 = MergeTourWithBestTour();
if (Cost < BestCost) {
BestCost = Cost;
RecordBetterTour();
RecordBestTour();
}
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, 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 (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);
do
Parent2 = LinearSelection(PopulationSize, 1.25);
while (Parent2 == Parent1);
ApplyCrossover(Parent1, Parent2);
N = FirstNode;
do {
if (ProblemType != HCP && ProblemType != HPP) {
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();
for (int i = 0; i < TSP_N; i++)
{
TSP_RESULT[i] = BestTour[i] - 1;
}
// printf("%d -¡·",BestTour[i]-1);
return BestCost;
}
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;
}
