void SolveKCenterSubproblems()
{
Node *N;
GainType GlobalBestCost, OldGlobalBestCost;
double EntryTime = GetTime();
int CurrentSubproblem, Subproblems;
AllocateStructures();
ReadPenalties();
/* Compute upper bound for the original problem */
GlobalBestCost = 0;
N = FirstNode;
do {
if (!Fixed(N, N->SubproblemSuc))
GlobalBestCost += Distance(N, N->SubproblemSuc);
N->Subproblem = 0;
}
while ((N = N->SubproblemSuc) != FirstNode);
if (TraceLevel >= 1) {
if (TraceLevel >= 2)
printff("\n");
printff("*** K-center partitioning *** [Cost = " GainFormat "]\n",
GlobalBestCost);
}
Subproblems = (int) ceil((double) Dimension / SubproblemSize);
KCenterClustering(Subproblems);
for (CurrentSubproblem = 1;
CurrentSubproblem <= Subproblems; CurrentSubproblem++) {
OldGlobalBestCost = GlobalBestCost;
SolveSubproblem(CurrentSubproblem, Subproblems, &GlobalBestCost);
if (SubproblemsCompressed && GlobalBestCost == OldGlobalBestCost)
SolveCompressedSubproblem(CurrentSubproblem, Subproblems,
&GlobalBestCost);
}
printff("\nCost = " GainFormat, GlobalBestCost);
if (Optimum != MINUS_INFINITY && Optimum != 0)
printff(", Gap = %0.4f%%",
100.0 * (GlobalBestCost - Optimum) / Optimum);
printff(", Time = %0.2f sec. %s\n", fabs(GetTime() - EntryTime),
GlobalBestCost < Optimum ? "<" : GlobalBestCost ==
Optimum ? "=" : "");
if (SubproblemBorders && Subproblems > 1)
SolveSubproblemBorderProblems(Subproblems, &GlobalBestCost);
}
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 SolveKarpSubproblems()
{
Node *N;
int i;
double EntryTime = GetTime();
AllocateStructures();
ReadPenalties();
/* Compute upper bound for the original problem */
GlobalBestCost = 0;
N = FirstNode;
do {
if (!Fixed(N, N->SubproblemSuc))
GlobalBestCost += Distance(N, N->SubproblemSuc);
N->Subproblem = 0;
}
while ((N = N->BestSuc = N->SubproblemSuc) != FirstNode);
if (TraceLevel >= 1) {
if (TraceLevel >= 2)
printff("\n");
printff("*** Karp partitioning *** [Cost = " GainFormat "]\n",
GlobalBestCost);
}
if (WeightType == GEO || WeightType == GEOM ||
WeightType == GEO_MEEUS || WeightType == GEOM_MEEUS) {
N = FirstNode;
do {
N->Xc = N->X;
N->Yc = N->Y;
N->Zc = N->Z;
if (WeightType == GEO || WeightType == GEO_MEEUS)
GEO2XYZ(N->Xc, N->Yc, &N->X, &N->Y, &N->Z);
else
GEOM2XYZ(N->Xc, N->Yc, &N->X, &N->Y, &N->Z);
} while ((N = N->SubproblemSuc) != FirstNode);
CoordType = THREED_COORDS;
}
KDTree = BuildKDTree();
if (WeightType == GEO || WeightType == GEOM ||
WeightType == GEO_MEEUS || WeightType == GEOM_MEEUS) {
N = FirstNode;
do {
N->X = N->Xc;
N->Y = N->Yc;
N->Z = N->Zc;
} while ((N = N->SubproblemSuc) != FirstNode);
CoordType = TWOD_COORDS;
}
CurrentSubproblem = 0;
Subproblems = 1;
for (i = Dimension - 1; i > SubproblemSize; i /= 2)
Subproblems *= 2;
RestrictedSearchSaved = RestrictedSearch;
KarpPartition(0, Dimension - 1, 0);
free(KDTree);
printff("\nCost = " GainFormat, GlobalBestCost);
if (Optimum != MINUS_INFINITY && Optimum != 0)
printff(", Gap = %0.3f%%",
100.0 * (GlobalBestCost - Optimum) / Optimum);
printff(", Time = %0.1f sec. %s\n", fabs(GetTime() - EntryTime),
GlobalBestCost < Optimum ? "<" : GlobalBestCost ==
Optimum ? "=" : "");
if (SubproblemBorders && Subproblems > 1)
SolveSubproblemBorderProblems(Subproblems);
}
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;
}
void SolveRoheSubproblems()
{
Node *N;
int CurrentSubproblem, Subproblems, Remaining, i;
GainType GlobalBestCost, OldGlobalBestCost;
double XMin, XMax, YMin, YMax, ZMin, ZMax, DX, DY, DZ, CLow, CMid,
CHigh;
double EntryTime = GetTime();
AllocateStructures();
ReadPenalties();
Subproblems = 0;
/* Compute upper bound for the original problem */
GlobalBestCost = 0;
N = FirstNode;
do {
if (!Fixed(N, N->SubproblemSuc))
GlobalBestCost += Distance(N, N->SubproblemSuc);
N->Subproblem = 0;
}
while ((N = N->SubproblemSuc) != FirstNode);
if (TraceLevel >= 1) {
if (TraceLevel >= 2)
printff("\n");
printff("*** Rohe partitioning *** [Cost = " GainFormat "]\n",
GlobalBestCost);
}
if (WeightType == GEO || WeightType == GEOM ||
WeightType == GEO_MEEUS || WeightType == GEOM_MEEUS) {
N = FirstNode;
do {
N->Xc = N->X;
N->Yc = N->Y;
N->Zc = N->Z;
if (WeightType == GEO || WeightType == GEO_MEEUS)
GEO2XYZ(N->Xc, N->Yc, &N->X, &N->Y, &N->Z);
else
GEOM2XYZ(N->Xc, N->Yc, &N->X, &N->Y, &N->Z);
} while ((N = N->SubproblemSuc) != FirstNode);
CoordType = THREED_COORDS;
}
N = FirstNode;
XMin = XMax = N->X;
YMin = YMax = N->Y;
ZMin = ZMax = N->Z;
while ((N = N->SubproblemSuc) != FirstNode) {
if (N->X < XMin)
XMin = N->X;
else if (N->X > XMax)
XMax = N->X;
if (N->Y < YMin)
YMin = N->Y;
else if (N->Y > YMax)
YMax = N->Y;
if (N->Z < ZMin)
ZMin = N->Z;
else if (N->Z > ZMax)
ZMax = N->Z;
}
KDTree = BuildKDTree(SubproblemSize);
Remaining = Dimension;
while (Remaining > SubproblemSize) {
N = FirstNode;
i = Random() % Remaining;
while (i--)
N = N->Suc;
DX = (0.5 + 0.5 * Random() / (PRANDMAX - 1)) * (XMax - XMin);
DY = (0.5 + 0.5 * Random() / (PRANDMAX - 1)) * (YMax - YMin);
DZ = (0.5 + 0.5 * Random() / (PRANDMAX - 1)) * (ZMax - ZMin);
CLow = 0;
CHigh = 2;
/* Binary search */
do {
CMid = (CLow + CHigh) / 2;
Size = 0;
WindowSize(N->X - CMid * DX, N->X + CMid * DX,
N->Y - CMid * DY, N->Y + CMid * DY,
N->Z - CMid * DZ, N->Z + CMid * DZ,
0, Dimension - 1);
if (Size >= 2.0 / 3 * SubproblemSize && Size <= SubproblemSize)
break;
if (Size < 2.0 / 3 * SubproblemSize)
CLow = CMid;
else
CHigh = CMid;
}
while (CHigh - CLow > DBL_EPSILON);
MakeSubproblem(N->X - CMid * DX, N->X + CMid * DX,
N->Y - CMid * DY, N->Y + CMid * DY,
N->Z - CMid * DZ, N->Z + CMid * DZ,
++Subproblems, 0, Dimension - 1);
Remaining -= Size;
}
if (Remaining > 3) {
Subproblems++;
N = FirstNode;
do
N->Subproblem = Subproblems;
while ((N = N->Suc) != FirstNode);
}
if (WeightType == GEO || WeightType == GEOM || WeightType == GEO_MEEUS
|| WeightType == GEOM_MEEUS) {
N = FirstNode;
do {
N->X = N->Xc;
N->Y = N->Yc;
N->Z = N->Zc;
} while ((N = N->SubproblemSuc) != FirstNode);
CoordType = TWOD_COORDS;
}
free(KDTree);
for (CurrentSubproblem = 1;
CurrentSubproblem <= Subproblems; CurrentSubproblem++) {
OldGlobalBestCost = GlobalBestCost;
SolveSubproblem(CurrentSubproblem, Subproblems, &GlobalBestCost);
if (SubproblemsCompressed && GlobalBestCost == OldGlobalBestCost)
SolveCompressedSubproblem(CurrentSubproblem, Subproblems,
&GlobalBestCost);
}
printff("\nCost = " GainFormat, GlobalBestCost);
if (Optimum != MINUS_INFINITY && Optimum != 0)
printff(", Gap = %0.4f%%",
100.0 * (GlobalBestCost - Optimum) / Optimum);
printff(", Time = %0.1f sec. %s\n", fabs(GetTime() - EntryTime),
GlobalBestCost < Optimum ? "<" : GlobalBestCost ==
Optimum ? "=" : "");
if (SubproblemBorders && Subproblems > 1)
SolveSubproblemBorderProblems(Subproblems, &GlobalBestCost);
}
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;
}
