public void start() {
System.out.println();
openga.util.timeClock timeClock1 = new openga.util.timeClock();
timeClock1.start();
GaMain.startGA();
timeClock1.end();
// System.out.println("\nThe final result");
int bestInd = getBestSolnIndex(GaMain.getArchieve());
System.out.println(GaMain.getArchieve().getSingleChromosome(bestInd).toString1());
String implementationResult = "";
implementationResult +=
fileName
+ "\t"
+ numberOfJob
+ "\t"
+ DEFAULT_crossoverRate
+ "\t"
+ DEFAULT_mutationRate
+ "\t"
+ DEFAULT_PopSize
+ "\t"
+ GaMain.getArchieve().getSingleChromosome(bestInd).getObjValue()[0]
+ "\t"
+ timeClock1.getExecutionTime() / 1000.0
+ "\n";
// implementationResult += fileName + "\t" + evaporationMethod + "\t" +
// GaMain.getArchieve().getSingleChromosome(bestInd).getObjValue()[0] + "\t" +
// timeClock1.getExecutionTime() / 1000.0 + "\n";
// implementationResult += fileName+"\t"+numberOfJob+"\t"+startingGeneration+"\t"+interval+"\t"+
// GaMain.getArchieve().getSingleChromosome(bestInd).getObjValue()[0]+"\t"+timeClock1.getExecutionTime()/1000.0+"\n";
writeFile("SingleMachine_PREDA1002", implementationResult);
System.out.println(implementationResult);
}
/**
* For single objective problem
*
* @param arch1
* @return
*/
public int getBestSolnIndex(populationI arch1) {
int index = 0;
double bestobj = Double.MAX_VALUE;
for (int k = 0; k < GaMain.getArchieve().getPopulationSize(); k++) {
if (bestobj > GaMain.getArchieve().getObjectiveValues(k)[0]) {
bestobj = GaMain.getArchieve().getObjectiveValues(k)[0];
index = k;
}
}
return index;
}
public void initiateVars() {
GaMain = new singleThreadGAwithProbabilityMatrixPREDA();
Population = new population();
Selection = new binaryTournament();
Crossover = new twoPointCrossover2(); //
Crossover2 = new PMX();
Mutation = new swapMutation();
// Mutation = new swapMutationWithMining2();//swapMutationWithMining2 shiftMutationWithMining2
Mutation2 = new inverseMutation();
ObjectiveFunction = new ObjectiveFunctionScheduleI[numberOfObjs];
ObjectiveFunction[0] =
new ObjectiveEarlinessTardinessPenalty(); // the first objective, tardiness,
// ObjectiveTardiness
// ObjectiveEarlinessTardinessPenalty
Fitness = new singleObjectiveFitness();
objectiveMinimization = new boolean[numberOfObjs];
objectiveMinimization[0] = true;
// objectiveMinimization[1] = true;
encodeType = true;
clone1 = new solutionVectorCloneWithMutation(); // swap mutation
// set schedule data to the objectives
ObjectiveFunction[0].setScheduleData(dueDay, processingTime, numberOfMachines);
// set the data to the GA main program.
GaMain.setLearningRate(lamda, beta); // Interaction Learning rate
GaMain.setWeight(w1, w2); // model=uni*w1 + bi*w2
GaMain.setProbabilityMatrixData(startingGeneration, interval);
GaMain.setSequenceStrategy(strategy);
totalSolnsToExamine = 125000;
DEFAULT_PopSize = 100;
// System.out.println(DEFAULT_PopSize);
// System.exit(0);
DEFAULT_generations = totalSolnsToExamine / (DEFAULT_PopSize);
GaMain.setEvaporationMethod(applyEvaporation, evaporationMethod);
GaMain.setData(
Population,
Selection,
Crossover,
Mutation,
ObjectiveFunction,
Fitness,
DEFAULT_generations,
DEFAULT_PopSize,
DEFAULT_PopSize,
numberOfJob,
DEFAULT_crossoverRate,
DEFAULT_mutationRate,
objectiveMinimization,
numberOfObjs,
encodeType,
elitism);
GaMain.setSecondaryCrossoverOperator(Crossover2, false);
GaMain.setSecondaryMutationOperator(Mutation2, false);
}