public Problem(OptimisationProblem problem, FitnessHandler fitnessFunction, double[] initialWeights)
{
this.fitnessFunction = fitnessFunction;
solution = new Position(initialWeights.Length);
SS = new SwarmSize(initialWeights.Length);
int d;
this.function = (int)problem;
this.epsilon = 0.00000; // Acceptable error (default). May be modified below
this.objective = 0; // Objective value (default). May be modified below
//this.evalMax =100000
// Define the solution point, for test
// NEEDED when param.stop = 2
// i.e. when stop criterion is distance_to_solution < epsilon
/*
for (d = 0; d < 30; d++)
{
this.solution.x[d] = 0;
}
*/
this.SS.q.size = this.SS.D;
this.SS.D = initialWeights.Length;// Dimension
for (d = 0; d < this.SS.D; d++)
{
this.SS.min[d] = -1; // -100
this.SS.max[d] = 1; // 100
this.SS.q.q[d] = 0; // Relative quantisation, in [0,1].
}
this.evalMax = int.MaxValue;// Max number of evaluations for each run
this.epsilon = 0.0; // 1e-3;
this.objective = 0;
// For test purpose, the initialisation space may be different from
// the search space. If so, just modify the code below
for (d = 0; d < this.SS.D; d++)
{
this.SS.maxInit[d] = this.SS.max[d];
this.SS.minInit[d] = this.SS.min[d];
}
}
public static void quantis(Position position, SwarmSize SS)
{
/*
Quantisatition of a position
Only values like x+k*q (k integer) are admissible
*/
int d;
double qd;
for (d = 0; d < position.size; d++)
{
qd = SS.q.q[d];
if (qd > 0.0) // Note that qd can't be < 0
{
//qd = qd * (SS.max[d] - SS.min[d]) / 2;
position.x[d] = qd * Math.Floor(0.5 + position.x[d] / qd);
}
}
}
public Problem()
{
solution = new Position();
SS = new SwarmSize();
}
public Problem(OptimisationProblem problem)
{
solution = new Position();
SS = new SwarmSize();
int d;
int nAtoms; // For Lennard-Jones problem
double[] lennard_jones = new[] { -1, -3, -6, -9.103852, -12.71, -16.505384, -19.821489, -24.113360, -28.422532, -32.77, -37.97, -44.33, -47.84, -52.32 };
this.function = (int)problem;
this.epsilon = 0.00000; // Acceptable error (default). May be modified below
this.objective = 0; // Objective value (default). May be modified below
// Define the solution point, for test
// NEEDED when param.stop = 2
// i.e. when stop criterion is distance_to_solution < epsilon
for (d = 0; d < 30; d++)
{
this.solution.x[d] = 0;
}
// ------------------ Search space
switch (this.function)
{
case 0: // Parabola
this.SS.D = 30;// Dimension
for (d = 0; d < this.SS.D; d++)
{
this.SS.min[d] = -100; // -100
this.SS.max[d] = 100; // 100
this.SS.q.q[d] = 0; // Relative quantisation, in [0,1].
}
this.evalMax = 100000;// Max number of evaluations for each run
this.epsilon = 0.0; // 1e-3;
this.objective = 0;
// For test purpose, the initialisation space may be different from
// the search space. If so, just modify the code below
for (d = 0; d < this.SS.D; d++)
{
this.SS.maxInit[d] = this.SS.max[d]; // May be a different value
this.SS.minInit[d] = this.SS.min[d]; // May be a different value
}
break;
case 100: // CEC 2005 F1
this.SS.D = 30;//30;
for (d = 0; d < this.SS.D; d++)
{
this.SS.min[d] = -100;
this.SS.max[d] = 100;
this.SS.q.q[d] = 0;
}
this.evalMax = this.SS.D * 10000;
this.epsilon = 0.000001; //Acceptable error
this.objective = -450; // Objective value
for (d = 0; d < this.SS.D; d++)
{
this.SS.maxInit[d] = this.SS.max[d];
this.SS.minInit[d] = this.SS.min[d];
}
break;
case 102: // Rosenbrock. CEC 2005 F6
this.SS.D = 10; // 10
// Boundaries
for (d = 0; d < this.SS.D; d++)
{
this.SS.min[d] = -100; this.SS.max[d] = 100;
this.SS.q.q[d] = 0;
}
this.evalMax = this.SS.D * 10000;
this.epsilon = 0.01; //0.01 Acceptable error
this.objective = 390;
for (d = 0; d < this.SS.D; d++)
{
this.SS.maxInit[d] = this.SS.max[d];
this.SS.minInit[d] = this.SS.min[d];
}
break;
case 103:// CEC 2005 F9, Rastrigin
this.SS.D = 30;
for (d = 0; d < this.SS.D; d++)
{
this.SS.min[d] = -5;
this.SS.max[d] = 5;
this.SS.q.q[d] = 0;
}
this.epsilon = 0.01; // 0.01; // Acceptable error
this.objective = -330; // Objective value
this.evalMax = this.SS.D * 10000;
for (d = 0; d < this.SS.D; d++)
{
this.SS.maxInit[d] = this.SS.max[d];
this.SS.minInit[d] = this.SS.min[d];
}
break;
case 104:// CEC 2005 F2 Schwefel
this.SS.D = 10;
for (d = 0; d < this.SS.D; d++)
{
this.SS.min[d] = -100;
this.SS.max[d] = 100;
this.SS.q.q[d] = 0;
}
this.epsilon = 0.00001; // Acceptable error
this.objective = -450; // Objective value
this.evalMax = this.SS.D * 10000;
for (d = 0; d < this.SS.D; d++)
{
this.SS.maxInit[d] = this.SS.max[d];
this.SS.minInit[d] = this.SS.min[d];
}
break;
case 105:// CEC 2005 F7 Griewank (NON rotated)
this.SS.D = 10; // 10
for (d = 0; d < this.SS.D; d++)
{
this.SS.min[d] = -600;
this.SS.max[d] = 600;
this.SS.q.q[d] = 0;
}
this.epsilon = 0.01; //Acceptable error
this.objective = -180; // Objective value
this.evalMax = this.SS.D * 10000;
for (d = 0; d < this.SS.D; d++)
{
this.SS.maxInit[d] = this.SS.max[d];
this.SS.minInit[d] = this.SS.min[d];
}
break;
case 106:// CEC 2005 F8 Ackley (NON rotated)
this.SS.D = 10;
for (d = 0; d < this.SS.D; d++)
{
this.SS.min[d] = -32;
this.SS.max[d] = 32;
this.SS.q.q[d] = 0;
}
this.epsilon = 0.0001; // Acceptable error
this.objective = -140; // Objective value
this.evalMax = this.SS.D * 10000;
for (d = 0; d < this.SS.D; d++)
{
this.SS.maxInit[d] = this.SS.max[d];
this.SS.minInit[d] = this.SS.min[d];
}
break;
/*
case 100: // Parabola
this.SS.D =10;// Dimension
for (d = 0; d < this.SS.D; d++)
{
this.SS.min[d] = -100; // -100
this.SS.max[d] = 100; // 100
this.SS.q.q[d] = 0; // Relative quantisation, in [0,1].
}
this.evalMax = 100000;// Max number of evaluations for each run
this.epsilon=0.00000;
for (d = 0; d < this.SS.D; d++)
{
this.SS.maxInit[d]=this.SS.max[d];
this.SS.minInit[d]=this.SS.min[d];
}
break;
*/
case 1: // Griewank
this.SS.D = 10;
// Boundaries
for (d = 0; d < this.SS.D; d++)
{
this.SS.min[d] = -100;
this.SS.max[d] = 100;
this.SS.q.q[d] = 0;
}
this.evalMax = 400000;
this.epsilon = 0.05;
this.objective = 0;
for (d = 0; d < this.SS.D; d++)
{
this.SS.maxInit[d] = this.SS.max[d];
this.SS.minInit[d] = this.SS.min[d];
}
break;
case 2: // Rosenbrock
this.SS.D = 30; // 30
// Boundaries
for (d = 0; d < this.SS.D; d++)
{
this.SS.min[d] = -30; // -30;
this.SS.max[d] = 30; // 30;
this.SS.q.q[d] = 0;
}
this.epsilon = 0;
this.evalMax = 300000; //2.e6; // 40000
this.objective = 0;
for (d = 0; d < this.SS.D; d++)
{
this.SS.maxInit[d] = 30; //this.SS.max[d];
this.SS.minInit[d] = 15; //this.SS.min[d];
}
break;
case 3: // Rastrigin
this.SS.D = 10;
// Boundaries
for (d = 0; d < this.SS.D; d++)
{
this.SS.min[d] = -5.12;
this.SS.max[d] = 5.12;
this.SS.q.q[d] = 0;
}
this.evalMax = 3200;
this.epsilon = 0.0;
this.objective = 0;
for (d = 0; d < this.SS.D; d++)
{
this.SS.minInit[d] = this.SS.min[d];
this.SS.maxInit[d] = this.SS.max[d];
}
break;
case 4: // Tripod
this.SS.D = 2; // Dimension
// Boundaries
for (d = 0; d < this.SS.D; d++)
{
this.SS.min[d] = -100;
this.SS.max[d] = 100;
this.SS.q.q[d] = 0;
}
this.evalMax = 10000;
this.epsilon = 0.0001;
for (d = 0; d < this.SS.D; d++)
{
this.SS.maxInit[d] = this.SS.max[d];
this.SS.minInit[d] = this.SS.min[d];
}
break;
case 5: // Ackley
this.SS.D = 10;
// Boundaries
for (d = 0; d < this.SS.D; d++)
{
this.SS.min[d] = -32; // 32
this.SS.max[d] = 32;
this.SS.q.q[d] = 0;
}
this.evalMax = 3200;
this.epsilon = 0.0;
this.objective = 0;
for (d = 0; d < this.SS.D; d++)
{
this.SS.maxInit[d] = this.SS.max[d];
this.SS.minInit[d] = this.SS.min[d];
}
break;
case 6: // Schwefel. Min on (A=420.8687, ..., A)
this.SS.D = 30;
//this.objective=-this.SS.D*420.8687*sin(Math.Sqrt(420.8687));
this.objective = -12569.5;
this.epsilon = 2569.5;
for (d = 0; d < this.SS.D; d++)
{
this.SS.min[d] = -500;
this.SS.max[d] = 500;
this.SS.q.q[d] = 0;
}
this.evalMax = 300000;
for (d = 0; d < this.SS.D; d++)
{
this.SS.maxInit[d] = this.SS.max[d];
this.SS.minInit[d] = this.SS.min[d];
}
break;
case 7: // Schwefel 1.2
this.SS.D = 40;
this.objective = 0;
for (d = 0; d < this.SS.D; d++)
{
this.SS.min[d] = -100;
this.SS.max[d] = 100;
this.SS.q.q[d] = 0;
}
this.evalMax = 40000;
for (d = 0; d < this.SS.D; d++)
{
this.SS.maxInit[d] = this.SS.max[d];
this.SS.minInit[d] = this.SS.min[d];
}
break;
case 8: // Schwefel 2.22
this.SS.D = 30;
this.objective = 0;
for (d = 0; d < this.SS.D; d++)
{
this.SS.min[d] = -10;
this.SS.max[d] = 10;
this.SS.q.q[d] = 0;
}
this.evalMax = 100000;
for (d = 0; d < this.SS.D; d++)
{
this.SS.maxInit[d] = this.SS.max[d];
this.SS.minInit[d] = this.SS.min[d];
}
break;
case 9: // Neumaier 3
this.SS.D = 40;
this.objective = 0;
for (d = 0; d < this.SS.D; d++)
{
this.SS.min[d] = -this.SS.D * this.SS.D;
this.SS.max[d] = -this.SS.min[d];
this.SS.q.q[d] = 0;
}
this.evalMax = 40000;
for (d = 0; d < this.SS.D; d++)
{
this.SS.maxInit[d] = this.SS.max[d];
this.SS.minInit[d] = this.SS.min[d];
}
break;
case 10: // G3 (constrained)
this.SS.D = 10;
for (d = 0; d < this.SS.D; d++)
{
this.SS.min[d] = 0;
this.SS.max[d] = 1;
this.SS.q.q[d] = 0;
}
this.evalMax = 340000;
this.objective = 0;
this.epsilon = 1e-6;
for (d = 0; d < this.SS.D; d++)
{
this.SS.maxInit[d] = this.SS.max[d];
this.SS.minInit[d] = this.SS.min[d];
}
break;
case 11: // Network
// btsNb=5; bcsNb=2;
btsNb = 19; bcsNb = 4;
this.SS.D = bcsNb * btsNb + 2 * bcsNb;
this.objective = 0;
for (d = 0; d < bcsNb * btsNb; d++) // Binary representation. 1 means: there is a link
{
this.SS.min[d] = 0;
this.SS.max[d] = 1;
this.SS.q.q[d] = 1;
}
for (d = bcsNb * btsNb; d < this.SS.D; d++) // 2D space for the BSC positions
{
this.SS.min[d] = 0;
this.SS.max[d] = 20; //15;
this.SS.q.q[d] = 0;
}
this.evalMax = 50;
this.objective = 0;
this.epsilon = 0;
break;
case 12: // Schwefel
this.SS.D = 30;
this.objective = 0;
for (d = 0; d < this.SS.D; d++)
{
this.SS.min[d] = -500;
this.SS.max[d] = 500;
this.SS.q.q[d] = 0;
}
this.evalMax = 200000;
for (d = 0; d < this.SS.D; d++)
{
this.SS.maxInit[d] = this.SS.max[d];
this.SS.minInit[d] = this.SS.min[d];
}
break;
case 13: // 2D Goldstein-Price function (f_min=3, on (0,-1))
this.SS.D = 2; // Dimension
this.objective = 0;
this.SS.min[0] = -100;
this.SS.max[0] = 100;
this.SS.q.q[0] = 0;
this.SS.min[1] = -100;
this.SS.max[1] = 100;
this.SS.q.q[1] = 0;
this.evalMax = 720;
for (d = 0; d < this.SS.D; d++)
{
this.SS.maxInit[d] = this.SS.max[d];
this.SS.minInit[d] = this.SS.min[d];
}
break;
case 14: // Schaffer f6
this.SS.D = 2; // Dimension
this.objective = 0;
this.SS.min[0] = -100;
this.SS.max[0] = 100;
this.SS.q.q[0] = 0;
this.SS.min[1] = -100;
this.SS.max[1] = 100;
this.SS.q.q[1] = 0;
this.evalMax = 4000;
for (d = 0; d < this.SS.D; d++)
{
this.SS.maxInit[d] = this.SS.max[d];
this.SS.minInit[d] = this.SS.min[d];
}
break;
case 15: // Step
this.SS.D = 20;
this.objective = 0;
for (d = 0; d < this.SS.D; d++)
{
this.SS.min[d] = -100;
this.SS.max[d] = 100;
this.SS.q.q[d] = 0;
}
this.evalMax = 2500;
for (d = 0; d < this.SS.D; d++)
{
this.SS.maxInit[d] = this.SS.max[d];
this.SS.minInit[d] = this.SS.min[d];
}
break;
case 16: // Schwefel 2.21
this.SS.D = 30;
this.objective = 0;
for (d = 0; d < this.SS.D; d++)
{
this.SS.min[d] = -100;
this.SS.max[d] = 100;
this.SS.q.q[d] = 0;
}
this.evalMax = 100000;
for (d = 0; d < this.SS.D; d++)
{
this.SS.maxInit[d] = this.SS.max[d];
this.SS.minInit[d] = this.SS.min[d];
}
break;
case 17: // Lennard-Jones
nAtoms = 2; // in {2, ..., 15}
this.SS.D = 3 * nAtoms; this.objective = lennard_jones[nAtoms - 2];
this.evalMax = 5000 + 3000 * nAtoms * (nAtoms - 1); // Empirical rule
this.epsilon = 1e-6;
// Note: with this acceptable error, nAtoms=10 seems to be the maximum
// possible value for a non-null success rate (5%)
//this.SS.D=3*21; this.objective=-81.684;
//this.SS.D=3*27; this.objective=-112.87358;
//this.SS.D=3*38; this.objective=-173.928427;
for (d = 0; d < this.SS.D; d++)
{
this.SS.min[d] = -2;
this.SS.max[d] = 2;
this.SS.q.q[d] = 0;
}
for (d = 0; d < this.SS.D; d++)
{
this.SS.maxInit[d] = this.SS.max[d];
this.SS.minInit[d] = this.SS.min[d];
}
break;
case 18: //Gear Train
this.SS.D = 4;
for (d = 0; d < this.SS.D; d++)
{
this.SS.min[d] = 12;
this.SS.max[d] = 60;
this.SS.q.q[d] = 1;
this.SS.maxInit[d] = this.SS.max[d];
this.SS.minInit[d] = this.SS.min[d];
}
this.evalMax = 20000;
this.epsilon = 1e-13;
this.objective = 2.7e-12;
break;
case 19: // Compression spring
this.constraint = 4;
this.SS.D = 3;
this.SS.min[0] = 1; this.SS.max[0] = 70; this.SS.q.q[0] = 1;
this.SS.min[1] = 0.6; this.SS.max[1] = 3; this.SS.q.q[1] = 0;
this.SS.min[2] = 0.207; this.SS.max[2] = 0.5; this.SS.q.q[2] = 0.001;
//for (d = 0; d < this.SS.D; d++)
//{
// this.SS.maxS[d] = this.SS.max[d];
// this.SS.minS[d] = this.SS.min[d];
//}
this.evalMax = 20000;
this.epsilon = 1e-10;
this.objective = 2.6254214578;
break;
case 99: // Test
this.SS.D = 2;
for (d = 0; d < this.SS.D; d++)
{
this.SS.min[d] = -100;
this.SS.max[d] = 100;
this.SS.q.q[d] = 0;
}
this.evalMax = 40000;
this.objective = 0.0;
this.epsilon = 0.00;
for (d = 0; d < this.SS.D; d++)
{
this.SS.maxInit[d] = this.SS.max[d];
this.SS.minInit[d] = this.SS.min[d];
}
break;
//TODO: Figure out why the following is unreachable
/*
// 2D Peaks function
this.SS.D = 2;
for (d = 0; d < this.SS.D; d++)
{
this.SS.min[d] = -3;
this.SS.max[d] = 3;
this.SS.q.q[d] = 0;
}
this.evalMax = 50000;
this.objective = -6.551133;
this.epsilon = 0.001;
for (d = 0; d < this.SS.D; d++)
{
this.SS.maxInit[d] = this.SS.max[d];
this.SS.minInit[d] = this.SS.min[d];
}
break;
// Quartic
this.SS.D = 50;
this.objective = 0;
for (d = 0; d < this.SS.D; d++)
{
this.SS.min[d] = -10;
this.SS.max[d] = 10;
this.SS.q.q[d] = 0;
}
this.evalMax = 25000;
for (d = 0; d < this.SS.D; d++)
{
this.SS.maxInit[d] = this.SS.max[d];
this.SS.minInit[d] = this.SS.min[d];
}
break;
this.SS.D = 2; // Dimension
this.objective = -2;
this.SS.min[0] = -2;
this.SS.max[0] = 2;
this.SS.q.q[0] = 0;
this.SS.min[1] = -3;
this.SS.max[1] = 3;
this.SS.q.q[1] = 0;
this.evalMax = 10000;
for (d = 0; d < this.SS.D; d++)
{
this.SS.maxInit[d] = this.SS.max[d];
this.SS.minInit[d] = this.SS.min[d];
}
break;
this.SS.D = 1; // Dimension
// Boundaries
for (d = 0; d < this.SS.D; d++)
{
this.SS.min[d] = -10;
this.SS.max[d] = 10;
this.SS.q.q[d] = 0;
}
this.objective = -1000; // Just a sure too small value for the above search space
this.evalMax = 1000;
for (d = 0; d < this.SS.D; d++)
{
this.SS.maxInit[d] = this.SS.max[d];
this.SS.minInit[d] = this.SS.min[d];
}
break;
*/
}
this.SS.q.size = this.SS.D;
}