public Hive(int totalNumberBees, int numberInactive, int numberActive, int numberScout, int maxNumberVisits, int maxNumberCycles, CitiesData citiesData, int BeginningAndEnd = 0)
{
//random = new Random(0);
this.BeginningAndEnd = BeginningAndEnd;
this.totalNumberBees = totalNumberBees;
this.numberInactive = numberInactive;
this.numberActive = numberActive;
this.numberScout = numberScout;
this.maxNumberVisits = maxNumberVisits;
this.maxNumberCycles = maxNumberCycles;
//this.maxCyclesWithNoImprovement = maxCyclesWithNoImprovement;
//this.citiesData = new CitiesData(citiesData.cities.Length); // hive's copy of problem-specific data
this.citiesData = citiesData; // reference to CityData
// this.probPersuasion & this.probMistake are hard-coded in class definition
this.bees = new Bee[totalNumberBees];
this.bestMemoryMatrix = GenerateRandomMemoryMatrix(); // alternative initializations are possible
this.bestMeasureOfQuality = MeasureOfQuality(this.bestMemoryMatrix);
this.indexesOfInactiveBees = new int[numberInactive]; // indexes of bees which are currently inactive
for (int i = 0; i < totalNumberBees; ++i) // initialize each bee, and best solution
{
int currStatus; // depends on i. need status before we can initialize Bee
if (i < numberInactive)
{
currStatus = 0; // inactive
indexesOfInactiveBees[i] = i; // curr bee is inactive
}
else if (i < numberInactive + numberScout)
{
currStatus = 2; // scout
}
else
{
currStatus = 1; // active
}
SubCalendarEvent[] randomMemoryMatrix = GenerateRandomMemoryMatrix();
double mq = MeasureOfQuality(randomMemoryMatrix);
int numberOfVisits = 0;
bees[i] = new Bee(currStatus, randomMemoryMatrix, mq, numberOfVisits); // instantiate current bee
// does this bee have best solution?
if (bees[i].measureOfQuality < bestMeasureOfQuality) // curr bee is better (< because smaller is better)
{
Array.Copy(bees[i].memoryMatrix, this.bestMemoryMatrix, bees[i].memoryMatrix.Length);
this.bestMeasureOfQuality = bees[i].measureOfQuality;
}
} // each bee
} // TravelingSalesmanHive ctor
public static Tuple<ICollection<SubCalendarEvent>, double> Run(ICollection<SubCalendarEvent> ListOfElements, int BeginningAndEnd=0)
{
#if EnableHive
if (ListOfElements.Count < 3)
{
return new Tuple<ICollection<SubCalendarEvent>, double>(ListOfElements, 0);
}
CitiesData citiesData = new CitiesData(ListOfElements.ToList());
int totalNumberBees = 100;
int numberInactive = 20;
int numberActive = 50;
int numberScout = 30;
int maxNumberVisits = 50;
int maxNumberCycles = 20;
Hive hive = new Hive(totalNumberBees, numberInactive, numberActive, numberScout, maxNumberVisits, maxNumberCycles, citiesData, BeginningAndEnd);
bool doProgressBar = false;
hive.Solve(doProgressBar);
return hive.getBestPath();
#endif
#if LinearTSP
Coordinate[] data = new Coordinate[ListOfElements.Count];
Dictionary<int, SubCalendarEvent> DictOFData = new Dictionary<int,SubCalendarEvent>();
int NameIndex = 0;
foreach (SubCalendarEvent eachSubCalendarEvent in ListOfElements)
{
data[NameIndex] = new Coordinate(NameIndex, eachSubCalendarEvent);
DictOFData.Add(NameIndex++, eachSubCalendarEvent);
}
SolverContext context = SolverContext.GetContext();
Model model = context.CreateModel();
// ------------
// Parameters
Set city = new Set(Domain.IntegerNonnegative, "city");
Parameter dist = new Parameter(Domain.Real, "dist", city, city);
var arcs = from p1 in data
from p2 in data
select new Arc { City1 = p1.Name, City2 = p2.Name, Distance = p1.Distance(p2, data.Length) };
dist.SetBinding(arcs, "Distance", "City1", "City2");
model.AddParameters(dist);
// ------------
// Decisions
Decision assign = new Decision(Domain.IntegerRange(0, 1), "assign", city, city);
Decision rank = new Decision(Domain.RealNonnegative, "rank", city);
model.AddDecisions(assign, rank);
// ------------
// Goal: minimize the length of the tour.
Goal goal = model.AddGoal("TourLength", GoalKind.Minimize,
Model.Sum(Model.ForEach(city, i => Model.ForEachWhere(city, j => dist[i, j] * assign[i, j], j => i != j))));
// ------------
// Enter and leave each city only once.
int N = data.Length;
model.AddConstraint("assign_1",
Model.ForEach(city, i => Model.Sum(Model.ForEachWhere(city, j => assign[i, j],
j => i != j)) == 1));
model.AddConstraint("assign_2",
Model.ForEach(city, j => Model.Sum(Model.ForEachWhere(city, i => assign[i, j], i => i != j)) == 1));
// Forbid subtours (Miller, Tucker, Zemlin - 1960...)
model.AddConstraint("no_subtours",
Model.ForEach(city,
i => Model.ForEachWhere(city,
j => rank[i] + 1 <= rank[j] + N * (1 - assign[i, j]),
j => Model.And(i != j, i >= 1, j >= 1)
)
)
);
Solution solution = context.Solve();
double Cost = goal.ToDouble();
List<SubCalendarEvent> OptimizedSubCalEvents = new List<SubCalendarEvent>();
var tour = from p in assign.GetValues() where (double)p[0] > 0.9 select p;
foreach (var i in tour.ToArray())
{
int MyIndex =Convert.ToInt32(i[2]);
OptimizedSubCalEvents.Add(DictOFData[MyIndex]);
//Console.WriteLine(i[1] + " -> " + );
}
context.ClearModel();
return new Tuple<ICollection<SubCalendarEvent>, double>(OptimizedSubCalEvents, Cost);
#endif
}
