int maxIterations = 2000;
int dimension = 2;
int popSize = 200;
double[] lowerBounds = new double[] { -2.048, -2.048 };
double[] upperBounds = new double[] { 2.048, 2.048 };
SimpleParticle finalSolution;

ParticleSwarm<SimpleParticle>.Solve(popSize, dimension, (solution, constraints) =>
{
	// this is the Rosenbrock Saddle cost function
	double[] positions = solution.Positions;
	double x0 = positions[0];
	double x1 = positions[1];

	double cost = 100 * Math.Pow(x0 * x0 - x1, 2) + Math.Pow(1 - x0, 2);
	return cost;
}, lowerBounds, upperBounds, out finalSolution, maxIterations);

int maxIterations = 2000;
int dimension = 2;
int displayEvery = 10;
double[] lowerBounds = new double[] { -2.048, -2.048 };
double[] upperBounds = new double[] { 2.048, 2.048 };
SimpleBee finalSolution;

BeeSwarm<SimpleBee>.Solve(dimension, (solution) =>
{
	// this is the Rosenbrock Saddle cost function
	
	double x0 = solution[0];
	double x1 = solution[1];

	double cost = 100 * Math.Pow(x0 * x0 - x1, 2) + Math.Pow(1 - x0, 2);
	return cost;
}, out finalSolution, lowerBounds, upperBounds, maxIterations, displayEvery);

int populationSize = 100;

SimpleAnt bestSolution;
TspBenchmark tsp = Tsp.get(Tsp.Instance.bayg29);
int problemSize = tsp.ProblemSize();
int displayEvery = 10;
int maxIterations = 1000;
AntColonySystem<SimpleAnt>.SolveByAntColonySystem(populationSize, problemSize
, solution => // this returns the cost of the solution which in the case of the TSP is the total distance of visiting every cities exactly once using the route represented by the solution 
{
	double cost = 0;
	for(int i=0; i < solution.Length; ++i)
	{
		int j = (i + 1) % solution.Length;
		int v = solution[i];
		int w = solution[j];
		cost += tsp.Distance(v, w);
	}
	return cost;
}, (state1, state2) => // this returns the heuristic value for a move from state1 to state2
{ 
	return 1.0 / (1.0 + tsp.Distance(state1, state2));
}, displayEvery, out bestSolution, null, maxIterations);

int populationSize = 100;
            
SimpleAnt bestSolution;
TspBenchmark tsp = Tsp.get(Tsp.Instance.bayg29);
int problemSize = tsp.ProblemSize();
int displayEvery = 10;
int maxIterations = 1000;
AntSystem<SimpleAnt>.SolveByAntSystem(populationSize, problemSize, solution =>
{
	double cost = 0;
	for(int i=0; i < solution.Length; ++i)
	{
		int j = (i + 1) % solution.Length;
		int v = solution[i];
		int w = solution[j];
		cost += tsp.Distance(v, w);
	}
	return cost;
}, (state1, state2) =>
{
	return 1.0 / (1.0 + tsp.Distance(state1, state2));
}, displayEvery, out bestSolution, null, maxIterations);