#include "Problem.h"#include "Solution.h"#include "Control.h"#include "Random.h"/*  orOptTSP.cpp  Program with the implementation of the function orOpt(), which performs  the Or opt local search for the TSP, on a VRPSD instance.  The initial solution given in input is modified  if a solution with  smaller expected cost is found.  The local optimum is with respect to the tsp length.*/#include <fstream.h>#include <iostream.h>using namespace std;void orOptTSP(Random* rnd, Control& control, Solution& initialSolution ){  //Read problem pointer from input solution.  Problem* pp = initialSolution.getProblem();  //Initialize current best solution.  Solution currSol(rnd, control, pp);  currSol.copySolution(initialSolution);  double trueCostBest = currSol.computeExpectedCost();  double currLen= currSol.computeTourLength();  //cout << "currSol.expectedCost " << currSol.expectedCost << endl;//////  //currSol.printOn(cout);/////	  //Allocation for the best move.  vector<int> bestMove(3,-1);  //The lengths of the neighbour solution.  double bestNeighborCost;  double neighborCost;  int stringLen = 3;  int falseImprove = 0; //Number of consecutive moves that lead to a worsening solution.  while(stringLen >= 1 && control.timeLeft() && falseImprove < 11){    currSol.firstMove(stringLen);    bestNeighborCost = currLen;    do{//Evaluate the move, update best move, and generate new move.      //Compute the neighbour cost by difference with the      //current solution cost.      neighborCost = currSol.computeMoveValueTSP(currLen);      //cout << "neighbCost " << neighborCost << endl;/////      //Update the  best move (with respect to tsp objective).      if( neighborCost  < bestNeighborCost ){	bestNeighborCost = neighborCost;	bestMove = currSol.getMove();      }    }while( currSol.nextMove(stringLen) );    //If an  improving move is found, update the solution.    if( bestNeighborCost < currLen ){      //Set the solution move equal to the best move.      currSol.setMove(bestMove);      //cout << "i " << bestMove[0] << " k " << bestMove[1] << " j " << bestMove[2] << endl;/////      double currCost = currSol.computeExpectedCost() ;      //Change the solution according to the "best" move, and re-compute the cost.      currSol.shiftTSP();      //      currSol.printOn(cout);/////      currLen= currSol.computeTourLength();      //cout << "orOptTSP(): currSol.expectedCost " << currSol.computeExpectedCost() << endl;////      //If the new current solution is really the new best, update the initial solution.      if ( currSol.computeExpectedCost() < trueCostBest ){	initialSolution.copySolution(currSol);	trueCostBest = currSol.expectedCost;	control.setCurrentCost( trueCostBest );      }      //Check-Set number of false improvements.      if(currSol.expectedCost > currCost) falseImprove ++;      else falseImprove = 0;     	    }    else //No best move has been found.      stringLen --;  }  //cout << "orOptTSP(): currSol.expectedCost " << currSol.computeExpectedCost() << endl;////}