public void TestLocalSearch()
{
var grasp = new GRASP(20);
var cities1 = new List<City> {new City(0, 0), new City(3, 3), new City(1, 1), new City(2, 2), new City(4, 4)};
var best = new Candidate();
best.Vector = new List<int>() {0, 1, 2, 3, 4};
best.Cost = grasp.Cost(best.Vector, cities1);
var rs = grasp.LocalSearch(best, cities1);
Assert.IsNotNull(rs);
Assert.IsNotNull(rs.Vector);
Assert.IsNotNull(rs.Cost);
Assert.AreNotSame(best, rs);
Assert.AreNotEqual(best.Vector, rs.Vector);
Assert.AreNotEqual(best.Cost, rs.Cost);
// No improvement
grasp = new GRASP(10);
best = new Candidate();
best.Vector = new List<int>() {0, 2, 3, 1, 4};
best.Cost = grasp.Cost(best.Vector, cities1);
rs = grasp.LocalSearch(best, cities1);
Assert.IsNotNull(rs);
Assert.AreEqual(best.Cost, rs.Cost);
}
public Candidate ConstructRandomizedGreedySolution(List<City> cities)
{
var candidate = new Candidate();
candidate.Vector = new List<int>();
candidate.Vector.Add(_random.Next(cities.Count));
var allCities = new List<int>();
for (int i = 0; i < cities.Count; i++)
{
allCities.Add(i);
}
// Build a path until we have all cities in the path
while (candidate.Vector.Count < cities.Count)
{
var candidates = allCities.Where(x=>!candidate.Vector.Contains(x)).ToArray();
var costs = new List<double>();
for (int i = 0; i < candidates.Length; i++)
{
costs.Add(Euc2d(cities[candidate.Vector.Last()], cities[i]));
}
var rcl = new List<int>();
var max = costs.Max();
var min = costs.Min();
for (int i = 0; i < costs.Count; i++)
{
if (costs[i] <= (min + _greedinessFactor*(max - min)))
{
rcl.Add(candidates[i]);
}
}
candidate.Vector.Add(rcl[_random.Next(rcl.Count)]);
}
candidate.Cost = Cost(candidate.Vector, cities);
return candidate;
}
/// <summary>
/// Main call for this
/// </summary>
public Candidate Search(List<City> cities)
{
// Create a cruddy dummy instance to immediately disregard
var best = new Candidate {Cost = int.MaxValue};
for (int i = 0; i < _maxIterations; i++)
{
var candidate = ConstructRandomizedGreedySolution(cities);
candidate = LocalSearch(candidate, cities);
best = (candidate.Cost < best.Cost) ? candidate : best;
Console.WriteLine(" > iteration #{0}, best=#{1}", i + 1, best.Cost);
}
return best;
}
public Candidate LocalSearch(Candidate best, List<City> cities)
{
int count = 0;
do
{
var candidate = new Candidate();
candidate.Vector = StochasticTwoOpt(best.Vector);
candidate.Cost = Cost(candidate.Vector, cities);
count = (candidate.Cost < best.Cost) ? 0 : count + 1;
if (candidate.Cost < best.Cost)
{
best = candidate;
}
} while (count <= _maxNoImprovements);
return best;
}