Ignore:
Timestamp:
12/20/17 23:35:23 (4 years ago)
Author:
abeham
Message:

#1614: finished checking the implementation against the paper

File:
1 edited

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  • branches/GeneralizedQAP/HeuristicLab.Problems.GeneralizedQuadraticAssignment/3.3/SolutionCreators/GreedyRandomizedSolutionCreator.cs

    r15553 r15555  
    3333
    3434namespace HeuristicLab.Problems.GeneralizedQuadraticAssignment {
     35  /// <summary>
     36  /// This is an implementation of the algorithm described in Mateus, G.R., Resende, M.G.C. & Silva, R.M.A. J Heuristics (2011) 17: 527. https://doi.org/10.1007/s10732-010-9144-0
     37  /// </summary>
    3538  [Item("GreedyRandomizedSolutionCreator", "Creates a solution according to the procedure described in Mateus, G., Resende, M., and Silva, R. 2011. GRASP with path-relinking for the generalized quadratic assignment problem. Journal of Heuristics 17, Springer Netherlands, pp. 527-565.")]
    3639  [StorableClass]
     
    6063    public static IntegerVector CreateSolution(IRandom random, GQAPInstance problemInstance,
    6164      int maximumTries, bool createMostFeasibleSolution, CancellationToken cancelToken) {
     65      var weights = problemInstance.Weights;
     66      var distances = problemInstance.Distances;
    6267      var demands = problemInstance.Demands;
    6368      var capacities = problemInstance.Capacities.ToArray();
     69      var transportCosts = problemInstance.TransportationCosts;
    6470      var equipments = demands.Length;
    6571      var locations = capacities.Length;
     
    6975      double minViolation = double.MaxValue;
    7076      int[] bestAssignment = null;
    71       var CF = new bool[equipments]; // set of chosen facilities / equipments
    72       var T = new List<int>(equipments); // set of facilities / equpiments that can be assigned to the set of chosen locations (CL)
     77      var F = new List<int>(equipments); // set of (initially) all facilities / equipments
     78      var CF = new List<int>(equipments); // set of chosen facilities / equipments
     79      var L = new List<int>(locations); // set of (initially) all locations
    7380      var CL_list = new List<int>(locations); // list of chosen locations
    7481      var CL_selected = new bool[locations]; // bool decision if location is chosen
    75       var F = new List<int>(equipments); // set of (initially) all facilities / equipments
    76       var L = new List<int>(locations); // set of (initially) all locations
    77      
     82      var T = new List<int>(equipments); // set of facilities / equpiments that can be assigned to the set of chosen locations (CL)
     83      var H = new double[locations]; // proportions for choosing locations in stage 1
     84      var W = new double[equipments]; // proportions for choosing facilities in stage 2
     85      var Z = new double[locations]; // proportions for choosing locations in stage 2
     86
    7887      while (maximumTries <= 0 || tries < maximumTries) {
    7988        cancelToken.ThrowIfCancellationRequested();
    8089
    81         Array.Copy(capacities, slack, locations);
    82         Array.Clear(CF, 0, equipments);
    83         Array.Clear(CL_selected, 0, locations);
    84         CL_list.Clear();
    85         T.Clear();
    86 
    87         F.Clear(); F.AddRange(Enumerable.Range(0, equipments));
    88         L.Clear(); L.AddRange(Enumerable.Range(0, locations));
    89 
    90         double threshold = 1.0;
    91         do {
    92           if (L.Count > 0 && random.NextDouble() < threshold) {
    93             int l = L.SampleRandom(random);
    94             L.Remove(l);
    95             CL_list.Add(l);
    96             CL_selected[l] = true;
    97             T = new List<int>(WhereDemandEqualOrLess(F, GetMaximumSlack(slack, CL_selected), demands));
    98           }
    99           if (T.Count > 0) {
    100             var fidx = Enumerable.Range(0, T.Count).SampleRandom(random);
    101             var f = T[fidx];
    102             T.RemoveAt(fidx);
    103             F.Remove(f); // TODO: slow
    104             CF[f] = true;
    105             int l = WhereSlackGreaterOrEqual(CL_list, demands[f], slack).SampleRandom(random);
    106             assignment[f] = l + 1;
     90        Array.Copy(capacities, slack, locations); // line 2 of Algorihm 2
     91        CF.Clear(); // line 2 of Algorihm 2
     92        Array.Clear(CL_selected, 0, locations); // line 2 of Algorihm 2
     93        CL_list.Clear(); // line 2 of Algorihm 2
     94        T.Clear(); // line 2 of Algorihm 2
     95
     96        F.Clear(); F.AddRange(Enumerable.Range(0, equipments)); // line 2 of Algorihm 2
     97        L.Clear(); L.AddRange(Enumerable.Range(0, locations)); // line 2 of Algorihm 2
     98
     99        double threshold = 1.0; // line 3 of Algorithm 2
     100        do { // line 4 of Algorithm 2
     101          if (L.Count > 0 && random.NextDouble() < threshold) { // line 5 of Algorithm 2
     102            // H is the proportion that a location is chosen
     103            // The paper doesn't mention what happens if the candidate list CL
     104            // does not contain an element in which case according to the formula
     105            // all H_k elements would be 0 which would be equal to random selection
     106            var HH = H.Select((v, i) => new { Index = i, Value = v })
     107              .Where(x => !CL_selected[x.Index])
     108              .Select(x => x.Value);
     109            int l = L.SampleProportional(random, 1, HH, false, false).Single(); // line 6 of Algorithm 2
     110            L.Remove(l); // line 7 of Algorithm 2
     111            CL_list.Add(l); // line 7 of Algorithm 2
     112            CL_selected[l] = true; // line 7 of Algorithm 2
     113            for (var k = 0; k < locations; k++)
     114              if (!CL_selected[k])
     115                H[k] += capacities[k] * capacities[l] / distances[k, l];
     116            T = new List<int>(WhereDemandEqualOrLess(F, GetMaximumSlack(slack, CL_selected), demands)); // line 8 of Algorithm 2
     117          }
     118          if (T.Count > 0) { // line 10 of Algorithm 2
     119            // W is the proportion that an equipment is chosen
     120            Array.Clear(W, 0, W.Length);
     121            var wk = 0;
     122            foreach (var k in T) {
     123              for (var h = 0; h < equipments; h++) {
     124                if (k == h) continue;
     125                W[wk] += weights[k, h];
     126              }
     127              W[wk] *= demands[k];
     128              wk++;
     129            }
     130            var f = T.SampleProportional(random, 1, W.Take(T.Count), false, false) // line 11 of Algorithm 2
     131              .Single();
     132            T.Remove(f); // line 12 of Algorithm 2
     133            F.Remove(f); // line 12 of Algorithm 2
     134            CF.Add(f); // line 12 of Algorithm 2
     135            var R = WhereSlackGreaterOrEqual(CL_list, demands[f], slack).ToList(); // line 13 of Algorithm 2
     136            // Z is the proportion that a location is chosen in stage 2
     137            Array.Clear(Z, 0, Z.Length);
     138            var zk = 0;
     139            foreach (var k in R) {
     140              // d is an increase in fitness if f would be assigned to location k
     141              var d = problemInstance.InstallationCosts[f, k];
     142              foreach (var i in CF) {
     143                if (assignment[i] == 0) continue; // i is unassigned
     144                var j = assignment[i] - 1;
     145                d += transportCosts * weights[f, i] * distances[k, j];
     146              }
     147              foreach (var h in CL_list) {
     148                if (k == h) continue;
     149                Z[zk] += slack[k] * capacities[h] / (d * distances[k, h]);
     150              }
     151              zk++;
     152            }
     153            int l = R.SampleProportional(random, 1, Z.Take(R.Count), false, false).Single(); // line 14 of Algorithm 2
     154            assignment[f] = l + 1; // line 15 of Algorithm 2
    107155            slack[l] -= demands[f];
    108             T = new List<int>(WhereDemandEqualOrLess(F, GetMaximumSlack(slack, CL_selected), demands));
    109             threshold = 1.0 - (double)T.Count / Math.Max(F.Count, 1.0);
    110           }
    111         } while (T.Count > 0 || L.Count > 0);
     156            T = new List<int>(WhereDemandEqualOrLess(F, GetMaximumSlack(slack, CL_selected), demands)); // line 16 of Algorithm 2
     157            threshold = 1.0 - (double)T.Count / Math.Max(F.Count, 1.0); // line 17 of Algorithm 2
     158          }
     159        } while (T.Count > 0 || L.Count > 0); // line 19 of Algorithm 2
     160
    112161        if (maximumTries > 0) tries++;
     162
    113163        if (F.Count == 0) {
    114           bestAssignment = assignment;
     164          bestAssignment = assignment.Select(x => x - 1).ToArray();
    115165          break;
    116166        } else if (createMostFeasibleSolution) {
     
    130180          double violation = slack.Select(x => x < 0 ? -x : 0).Sum();
    131181          if (violation < minViolation) {
    132             bestAssignment = assignment;
    133             assignment = new int[equipments];
     182            bestAssignment = assignment.Select(x => x - 1).ToArray();
    134183            minViolation = violation;
    135184          }
     185          assignment = new int[equipments];
    136186        }
    137187      }
    138188
    139       if (bestAssignment == null || bestAssignment.Any(x => x == 0))
     189      if (bestAssignment == null)
    140190        throw new InvalidOperationException(String.Format("No solution could be found in {0} tries.", maximumTries));
    141191
    142       return new IntegerVector(bestAssignment.Select(x => x - 1).ToArray());
     192      return new IntegerVector(bestAssignment);
    143193    }
    144194
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