randomrestart.cpp

vrpsd 4---------------------------------------------------------

#include "Problem.h"
#include "Solution.h"
#include "orOptTSP.h"
#include "Control_fr.h"
#include "Random.h"
#include "farthestInsertion.h"

/*
  randomRestart.cpp
  Program which performs orOpt local search on random initial solution, for ten times.
  Output: best result found, average CPU time for getting into a local optimum*10.
 */

#include <fstream.h>
#include <iostream.h>
#include <math.h>
#include <limits.h>

using namespace std;

int main( int argc, char** argv ) {

    
  //Create a control object from the command line arguments.
  Control_fr control( argc, argv );

  //Random object.
  Random* rnd = new Random((unsigned) control.getSeed());

  ostream& output = control.getOutputStream();
  int niter = control.getNumberOfIterations();
  double best=1000000000;
    
  //Initialize the problem instance, using input file stream and input data given by control.
  Problem *problem = new Problem( control );

  //Run a number of tries, control knows how many tries there should be done
  //Tell control we are starting a new try.
  control.beginTry();    
  Solution currentSolution( rnd,  control, problem );
  Solution bestSolution( rnd, control, problem );

  while( (control.getIterations() < niter) & control.timeLeft()) {
    
    //Initialize starting solution.
    randomizedFarthestInsertion(rnd, currentSolution);
    control.setCurrentCost( currentSolution.computeExpectedCost());

    //orOpt local search may improve currentSolution.
    orOptTSP(rnd, control, currentSolution);
    
    if(currentSolution.expectedCost < best) {
      best = currentSolution.expectedCost;
      bestSolution.copySolution(currentSolution);
    }
    control.incrementIterations();
  }
  control.endTry();
  bestSolution.printOn(output);

  output << "iterations: " << control.getIterations() << endl;
  output << "time:" << control.getTime() << endl;
  
  /*  //Initialize thresholds vector.
      vector<int> thresholds(problem->numberOfCustomers, -1);
      currentSolution.computeExpectedCostAndThresholds(thresholds);
      currentSolution.printOn(cout, thresholds);  
  */
}