private static IEnumerable <IndividualBase> SPEA2Test(ProblemBase mainProblem)
{
MultiObjectiveGeneticAlgorithm SPEA2 = new SPEA2(
mainProblem,
200,
200,
400,
10);
return(SPEA2.Execute());
}
/// <summary>
/// multi objective SPEA-2 for Emilie
/// </summary>
/// <param name="args"></param>
static void MultiObjective_Main(string[] args)
{
int nVar = 5; //number of decision variables
int mObj = 3; //number of objectives
double[] lb = new double[nVar]; //lower bound values for each variable
double[] ub = new double[nVar]; //upper bound values for each variable
for (int i = 0; i < nVar; i++) //here I'm assigning lower bound 0 and upper bound 1 to every variable
{
lb[i] = 0;
ub[i] = 10;
}
bool[] intX = new bool[nVar];
//intX[0] = true;
//intX[1] = true;
//intX[2] = true;
//intX[3] = true;
//intX[4] = true;
//intX[5] = false;
//intX[6] = false;
//intX[7] = false;
//intX[8] = false;
//intX[9] = false;
//intX[10] = false;
//intX[11] = false;
Func <double[], double[]> testfunc = testfnc; //evaluation function
//int randomSeed = ((int)DateTime.Now.Ticks & 0x0000FFFF); // random seed for random number generator
int randomSeed = 1;
//SPEA2
List <double[]> x0pop = new List <double[]>();
//x0pop.Add(new double[12] { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 }); //initial solution 1
//x0pop.Add(new double[12] { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 }); //initial solution 2
//x0pop.Add(new double[5] { 0, 0, 0, 0, 0 }); //initial solution 1
//x0pop.Add(new double[5] { 1, 1, 1, 1, 1 }); //initial solution 2
SPEA2 spea2 = new SPEA2(mObj, nVar, lb, ub, testfunc, randomSeed, x0pop, intX);
//change solver parameters
spea2.ga_genmax = 3000; // max amount of generations (which are the solver iterations)
spea2.ga_nPop = 20; //population size: number of solution candidates per iteration
spea2.ga_PCrossover = 0.8;
spea2.ga_PMutation = 0.2;
//initialize solver
Console.WriteLine("Initializing Algorithm:... {0}", spea2.GetSolverName());
Console.WriteLine("On Problem:... {0}", "MO.MOM_DTLZ4");
spea2.initialize();
Console.ReadKey();
//foreach (double[] current_best_objvalues in spea2.objvalsBestPop_MO)
//{
// Console.WriteLine("{0}, {1}, {2}", current_best_objvalues[0], current_best_objvalues[1], current_best_objvalues[2]);
//}
////show decision variables for each solution
//int j = 0;
//foreach (double[] current_best_x in spea2.xPopulationArchive)
//{
// Console.WriteLine(" ");
// Console.Write("individual {0}, x: ", j);
// for (int n = 0; n < spea2.nVar; n++)
// {
// Console.Write("{0}, ", current_best_x[n]);
// }
// j++;
//}
//for each generation
for (int t = 0; t < 100; t++)
{
//solve one generation
Console.WriteLine("***************************");
Console.WriteLine("Generation: {0}", t);
spea2.Solve(1);
//looping through pareto front of the current generation
//show three objective function values for each solution
foreach (double[] current_best_objvalues in spea2.objvalsBestPop_MO)
{
Console.WriteLine("{0}, {1}, {2}", current_best_objvalues[0], current_best_objvalues[1], current_best_objvalues[2]);
}
////show decision variables for each solution
//int j = 0;
//foreach (double[] current_best_x in spea2.xPopulationArchive)
//{
// Console.WriteLine(" ");
// Console.Write("individual {0}, x: ", j);
// for (int n = 0; n < spea2.nVar; n++)
// {
// Console.Write("{0}, ", current_best_x[n]);
// }
// j++;
//}
Console.WriteLine(" ");
Console.ReadKey();
}
Console.WriteLine(" ");
Console.ReadKey();
}
