public void ShowQualityIndicatorWindow(bool bshow)
{
qualityIndicatoWindow.ShowWindow = bshow;
qualityIndicatoWindow.Refresh();
if (bshow)
{
qualityIndicatoWindow.UpdateWindow();
}
else
{
_lastQuality = QualityIndicator.Unknown;
}
}
/// <summary>
///Usage: three choices
/// - AbYSS
/// - AbYSS problemName
/// - AbYSS problemName paretoFrontFile
/// </summary>
/// <param name="args">Command line arguments.</param>
public static void Main(string[] args)
{
Problem problem; // The problem to solve
Algorithm algorithm; // The algorithm to use
Operator crossover; // Crossover operator
Operator mutation; // Mutation operator
Operator improvement; // Operator for improvement
Dictionary <string, object> parameters; // Operator parameters
QualityIndicator indicators; // Object to get quality indicators
// Logger object and file to store log messages
var logger = Logger.Log;
var appenders = logger.Logger.Repository.GetAppenders();
var fileAppender = appenders[0] as log4net.Appender.FileAppender;
fileAppender.File = "AbYSS.log";
fileAppender.ActivateOptions();
indicators = null;
if (args.Length == 1)
{
object[] param = { "Real" };
problem = ProblemFactory.GetProblem(args[0], param);
}
else if (args.Length == 2)
{
object[] param = { "Real" };
problem = ProblemFactory.GetProblem(args[0], param);
indicators = new QualityIndicator(problem, args[1]);
}
else
{ // Default problem
problem = new Kursawe("Real", 3);
//problem = new Kursawe("BinaryReal", 3);
//problem = new Water("Real");
//problem = new ZDT4("ArrayReal", 10);
//problem = new ConstrEx("Real");
//problem = new DTLZ1("Real");
//problem = new OKA2("Real") ;
}
// STEP 2. Select the algorithm (AbYSS)
algorithm = new JMetalCSharp.Metaheuristics.AbYSS.AbYSS(problem);
// STEP 3. Set the input parameters required by the metaheuristic
algorithm.SetInputParameter("populationSize", 20);
algorithm.SetInputParameter("refSet1Size", 10);
algorithm.SetInputParameter("refSet2Size", 10);
algorithm.SetInputParameter("archiveSize", 100);
algorithm.SetInputParameter("maxEvaluations", 25000);
// STEP 4. Specify and configure the crossover operator, used in the
// solution combination method of the scatter search
parameters = new Dictionary <string, object>();
parameters.Add("probability", 0.9);
parameters.Add("distributionIndex", 20.0);
crossover = CrossoverFactory.GetCrossoverOperator("SBXCrossover", parameters);
// STEP 5. Specify and configure the improvement method. We use by default
// a polynomial mutation in this method.
parameters = new Dictionary <string, object>();
parameters.Add("probability", 1.0 / problem.NumberOfVariables);
parameters.Add("distributionIndex", 20.0);
mutation = MutationFactory.GetMutationOperator("PolynomialMutation", parameters);
parameters = new Dictionary <string, object>();
parameters.Add("improvementRounds", 1);
parameters.Add("problem", problem);
parameters.Add("mutation", mutation);
improvement = new MutationLocalSearch(parameters);
// STEP 6. Add the operators to the algorithm
algorithm.AddOperator("crossover", crossover);
algorithm.AddOperator("improvement", improvement);
long initTime = Environment.TickCount;
// STEP 7. Run the algorithm
SolutionSet population = algorithm.Execute();
long estimatedTime = Environment.TickCount - initTime;
// STEP 8. Print the results
logger.Info("Total execution time: " + estimatedTime + "ms");
logger.Info("Variables values have been writen to file VAR");
population.PrintVariablesToFile("VAR");
logger.Info("Objectives values have been writen to file FUN");
population.PrintObjectivesToFile("FUN");
Console.WriteLine("Total execution time: " + estimatedTime + "ms");
Console.ReadLine();
if (indicators != null)
{
logger.Info("Quality indicators");
logger.Info("Hypervolume: " + indicators.GetHypervolume(population));
logger.Info("GD : " + indicators.GetGD(population));
logger.Info("IGD : " + indicators.GetIGD(population));
logger.Info("Spread : " + indicators.GetSpread(population));
logger.Info("Epsilon : " + indicators.GetEpsilon(population));
}
}
/// <summary>
/// Usage: three options
/// - SPEA2
/// - SPEA2 problemName
/// - SPEA2 problemName ParetoFrontFile
/// </summary>
/// <param name="args">Command line arguments. The first (optional) argument specifies the problem to solve.</param>
public static void Main(string[] args)
{
Problem problem; // The problem to solve
Algorithm algorithm; // The algorithm to use
Operator crossover; // Crossover operator
Operator mutation; // Mutation operator
Operator selection; // Selection operator
QualityIndicator indicators; // Object to get quality indicators
Dictionary <string, object> parameters; // Operator parameters
// Logger object and file to store log messages
var logger = Logger.Log;
var appenders = logger.Logger.Repository.GetAppenders();
var fileAppender = appenders[0] as log4net.Appender.FileAppender;
fileAppender.File = "SPEA2.log";
fileAppender.ActivateOptions();
indicators = null;
if (args.Length == 1)
{
object[] param = { "Real" };
problem = ProblemFactory.GetProblem(args[0], param);
}
else if (args.Length == 2)
{
object[] param = { "Real" };
problem = ProblemFactory.GetProblem(args[0], param);
indicators = new QualityIndicator(problem, args[1]);
}
else
{ // Default problem
problem = new Kursawe("Real", 3);
//problem = new Water("Real");
//problem = new ZDT1("ArrayReal", 1000);
//problem = new ZDT4("BinaryReal");
//problem = new WFG1("Real");
//problem = new DTLZ1("Real");
//problem = new OKA2("Real") ;
}
algorithm = new JMetalCSharp.Metaheuristics.SPEA2.SPEA2(problem);
// Algorithm parameters
algorithm.SetInputParameter("populationSize", 100);
algorithm.SetInputParameter("archiveSize", 100);
algorithm.SetInputParameter("maxEvaluations", 25000);
// Mutation and Crossover for Real codification
parameters = new Dictionary <string, object>();
parameters.Add("probability", 0.9);
parameters.Add("distributionIndex", 20.0);
crossover = CrossoverFactory.GetCrossoverOperator("SBXCrossover", parameters);
parameters = new Dictionary <string, object>();
parameters.Add("probability", 1.0 / problem.NumberOfVariables);
parameters.Add("distributionIndex", 20.0);
mutation = MutationFactory.GetMutationOperator("PolynomialMutation", parameters);
// Selection operator
parameters = null;
selection = SelectionFactory.GetSelectionOperator("BinaryTournament", parameters);
// Add the operators to the algorithm
algorithm.AddOperator("crossover", crossover);
algorithm.AddOperator("mutation", mutation);
algorithm.AddOperator("selection", selection);
// Execute the algorithm
long initTime = Environment.TickCount;
SolutionSet population = algorithm.Execute();
long estimatedTime = Environment.TickCount - initTime;
// Result messages
logger.Info("Total execution time: " + estimatedTime + "ms");
logger.Info("Objectives values have been writen to file FUN");
population.PrintObjectivesToFile("FUN");
logger.Info("Variables values have been writen to file VAR");
population.PrintVariablesToFile("VAR");
if (indicators != null)
{
logger.Info("Quality indicators");
logger.Info("Hypervolume: " + indicators.GetHypervolume(population));
logger.Info("GD : " + indicators.GetGD(population));
logger.Info("IGD : " + indicators.GetIGD(population));
logger.Info("Spread : " + indicators.GetSpread(population));
logger.Info("Epsilon : " + indicators.GetEpsilon(population));
}
Console.WriteLine("Total execution time: " + estimatedTime + "ms");
Console.ReadLine();
}
/// <summary>
/// Runs the NSGA-II algorithm.
/// </summary>
/// <returns>a <code>SolutionSet</code> that is a set of non dominated solutions as a result of the algorithm execution</returns>
public override SolutionSet Execute()
{
// !!!! NEEDED PARAMETES !!! start
//J* Parameters
int populationSize = -1; // J* store population size
int maxEvaluations = -1; // J* store number of max Evaluations
int evaluations; // J* number of current evaluations
// J* Objects needed to illustrate the use of quality indicators inside the algorithms
QualityIndicator indicators = null; // QualityIndicator object
int requiredEvaluations; // Use in the example of use of the
// indicators object (see below)
// J* populations needed to implement NSGA-II
SolutionSet population; //J* Current population
SolutionSet offspringPopulation; //J* offspring population
SolutionSet union; //J* population resultant from current and offpring population
//J* Genetic Operators
Operator mutationOperator;
Operator crossoverOperator;
Operator selectionOperator;
//J* Used to evaluate crowding distance
Distance distance = new Distance();
//J* !!!! NEEDED PARAMETES !!! end
//J* !!! INITIALIZING PARAMETERS - start !!!
//Read the parameters
JMetalCSharp.Utils.Utils.GetIntValueFromParameter(this.InputParameters, "maxEvaluations", ref maxEvaluations); //J* required
JMetalCSharp.Utils.Utils.GetIntValueFromParameter(this.InputParameters, "populationSize", ref populationSize); //J* required
JMetalCSharp.Utils.Utils.GetIndicatorsFromParameters(this.InputParameters, "indicators", ref indicators); //J* optional
//Initialize the variables
population = new SolutionSet(populationSize);
evaluations = 0;
requiredEvaluations = 0;
//Read the operators
mutationOperator = Operators["mutation"];
crossoverOperator = Operators["crossover"];
selectionOperator = Operators["selection"];
//J* !!! INITIALIZING PARAMETERS - end !!!
//J* !!! Creating first population !!!
JMetalRandom.SetRandom(comp.MyRand);
comp.LogAddMessage("Random seed = " + comp.Seed);
// Create the initial solutionSet
Solution newSolution;
for (int i = 0; i < populationSize; i++)
{
newSolution = new Solution(Problem);
Problem.Evaluate(newSolution);
Problem.EvaluateConstraints(newSolution);
evaluations++;
population.Add(newSolution);
}
// Generations
while (evaluations < maxEvaluations)
{
// Create the offSpring solutionSet
offspringPopulation = new SolutionSet(populationSize);
Solution[] parents = new Solution[2];
for (int i = 0; i < (populationSize / 2); i++)
{
if (evaluations < maxEvaluations)
{
//obtain parents
parents[0] = (Solution)selectionOperator.Execute(population);
parents[1] = (Solution)selectionOperator.Execute(population);
Solution[] offSpring = (Solution[])crossoverOperator.Execute(parents);
mutationOperator.Execute(offSpring[0]);
mutationOperator.Execute(offSpring[1]);
Problem.Evaluate(offSpring[0]);
Problem.EvaluateConstraints(offSpring[0]);
Problem.Evaluate(offSpring[1]);
Problem.EvaluateConstraints(offSpring[1]);
offspringPopulation.Add(offSpring[0]);
offspringPopulation.Add(offSpring[1]);
evaluations += 2;
}
}
// Create the solutionSet union of solutionSet and offSpring
union = ((SolutionSet)population).Union(offspringPopulation);
// Ranking the union
Ranking ranking = new Ranking(union);
int remain = populationSize;
int index = 0;
SolutionSet front = null;
population.Clear();
// Obtain the next front
front = ranking.GetSubfront(index);
while ((remain > 0) && (remain >= front.Size()))
{
//Assign crowding distance to individuals
distance.CrowdingDistanceAssignment(front, Problem.NumberOfObjectives);
//Add the individuals of this front
for (int k = 0; k < front.Size(); k++)
{
population.Add(front.Get(k));
}
//Decrement remain
remain = remain - front.Size();
//Obtain the next front
index++;
if (remain > 0)
{
front = ranking.GetSubfront(index);
}
}
// Remain is less than front(index).size, insert only the best one
if (remain > 0)
{ // front contains individuals to insert
distance.CrowdingDistanceAssignment(front, Problem.NumberOfObjectives);
front.Sort(new CrowdingComparator());
for (int k = 0; k < remain; k++)
{
population.Add(front.Get(k));
}
remain = 0;
}
// This piece of code shows how to use the indicator object into the code
// of NSGA-II. In particular, it finds the number of evaluations required
// by the algorithm to obtain a Pareto front with a hypervolume higher
// than the hypervolume of the true Pareto front.
if ((indicators != null) && (requiredEvaluations == 0))
{
double HV = indicators.GetHypervolume(population);
if (HV >= (0.98 * indicators.TrueParetoFrontHypervolume))
{
requiredEvaluations = evaluations;
}
}
}
// Return as output parameter the required evaluations
SetOutputParameter("evaluations", requiredEvaluations);
comp.LogAddMessage("Evaluations = " + evaluations);
// Return the first non-dominated front
Ranking rank = new Ranking(population);
Result = rank.GetSubfront(0);
return(Result);
}
public void GetIndicatorForScore_WithCustomValues(int score, int?passScore, int?warnScore, QualityIndicator expectedResult)
{
// Arrange
this.configurationRepository.Setup(r => r.ReadValue <int>(ConfigurationKeys.PassScore)).Returns(passScore);
this.configurationRepository.Setup(r => r.ReadValue <int>(ConfigurationKeys.WarnScore)).Returns(warnScore);
//Act
var result = this.qualityIndicatorService.Object.GetIndicatorForScore(score);
// Assert
Assert.That(result, Is.EqualTo(expectedResult));
}
public SolutionSet Mix()
{
QualityIndicator indicators = new QualityIndicator(problem, fileRead.Qi); // QualityIndicator object
int requiredEvaluations = 0; // Use in the example of use of the
// indicators object (see below)
evaluations = 0;
iteration = 0;
populationSize = int.Parse(fileRead.Ps);
iterationsNumber = int.Parse(fileRead.Itn);
dataDirectory = "Data/Parameters/Weight";
int allR = int.Parse(fileRead.DERa) + int.Parse(fileRead.SBXRa) + int.Parse(fileRead.ACORa);
double ACOR = (double)int.Parse(fileRead.ACORa) / allR;
double DER = (double)int.Parse(fileRead.DERa) / allR;
Logger.Log.Info("POPSIZE: " + populationSize);
Console.WriteLine("POPSIZE: " + populationSize);
population = new SolutionSet(populationSize);
indArray = new Solution[problem.NumberOfObjectives];
t = int.Parse(fileRead.T);
nr = int.Parse(fileRead.Nr);
delta = double.Parse(fileRead.Delta);
gamma = double.Parse(fileRead.Gamma);
neighborhood = new int[populationSize][];
for (int i = 0; i < populationSize; i++)
{
neighborhood[i] = new int[t];
}
z = new double[problem.NumberOfObjectives];
//znad = new double[Problem.NumberOfObjectives];
lambda = new double[populationSize][];
for (int i = 0; i < populationSize; i++)
{
lambda[i] = new double[problem.NumberOfObjectives];
}
/**/ string dir = "Result/" + fileRead.Al;
if (fileRead.DERa != "0")
{
dir = dir + "_DifferentialEvolutionCrossover";
}
if (fileRead.SBXRa != "0")
{
dir = dir + "_SBXCrossover";
}
if (fileRead.ACORa != "0")
{
dir = dir + "_ACOR";
}
dir = dir + "/" + fileRead.Pb + "_" + fileRead.St + "/Record/DE(" + int.Parse(fileRead.DERa) + ")+SBX(" + int.Parse(fileRead.SBXRa) + ")+ACOR(" + int.Parse(fileRead.ACORa) + ")_NoMutation/nr=" + fileRead.Nr + "/ACOR+SBX";
if (Directory.Exists(dir))
{
Console.WriteLine("The directory {0} already exists.", dir);
}
else
{
Directory.CreateDirectory(dir);
Console.WriteLine("The directory {0} was created.", dir);
}
//Step 1. Initialization
//Step 1.1 Compute euclidean distances between weight vectors and find T
InitUniformWeight();
InitNeighborhood();
//Step 1.2 Initialize population
InitPoputalion();
//Step 1.3 Initizlize z
InitIdealPoint();
//Step 2 Update
for (int a = 1; a <= iterationsNumber; a++)
{
int[] permutation = new int[populationSize];
JMetalCSharp.Metaheuristics.MOEAD.Utils.RandomPermutation(permutation, populationSize);
Solution[] parents = new Solution[2];
int t = 0;
if (a <= (ACOR)*iterationsNumber)
{
//ACOR
for (int i = 0; i < populationSize; i++)
{
int n = permutation[i];
// or int n = i;
int type;
double rnd = JMetalRandom.NextDouble();
// STEP 2.1. ACOR selection based on probability
if (rnd < gamma) // if (rnd < realb)
{
type = 1; // minmum
//parents[0] = population.Get(ACOrSelection2(n, type, pro_T));
}
else
{
type = 2; // whole neighborhood probability
//parents[0] = population.Get(ACOrSelection2(n, type, pro_A));
}
GetStdDev(neighborhood);
//GetStdDev1(neighborhood, type);
//List<int> p = new List<int>();
//MatingSelection(p, n, 1, type);
// STEP 2.2. Reproduction
Solution child;
parents[0] = population.Get(ACOrSelection(n, type));
parents[1] = population.Get(n);
//parents[0] = population.Get(p[0]);
// Apply ACOR crossover
child = (Solution)crossover[2].Execute(parents);
child.NumberofReplace = t;
// Apply mutation
// mutation.Execute(child);
// Evaluation
problem.Evaluate(child);
evaluations++;
// STEP 2.3. Repair. Not necessary
// STEP 2.4. Update z_
UpdateReference(child);
// STEP 2.5. Update of solutions
t = UpdateProblemWithReplace(child, n, 1);
}
}
else if (a >= (1 - DER) * iterationsNumber)
{
//DE
for (int i = 0; i < populationSize; i++)
{
int n = permutation[i]; // or int n = i;
int type;
double rnd = JMetalRandom.NextDouble();
// STEP 2.1. Mating selection based on probability
if (rnd < delta) // if (rnd < realb)
{
type = 1; // neighborhood
}
else
{
type = 2; // whole population
}
List <int> p = new List <int>();
MatingSelection(p, n, 2, type);
// STEP 2.2. Reproduction
Solution child;
Solution[] parent = new Solution[3];
parent[0] = population.Get(p[0]);
parent[1] = population.Get(p[1]);
parent[2] = population.Get(n);
// Apply DE crossover
child = (Solution)crossover[0].Execute(new object[] { population.Get(n), parent });
// Apply mutation
mutation.Execute(child);
// Evaluation
problem.Evaluate(child);
evaluations++;
// STEP 2.3. Repair. Not necessary
// STEP 2.4. Update z_
UpdateReference(child);
// STEP 2.5. Update of solutions
UpdateProblem(child, n, type);
}
}
else
{
//SBX
//Create the offSpring solutionSet
SolutionSet offspringPopulation = new SolutionSet(populationSize);
for (int i = 0; i < (populationSize / 2); i++)
{
int n = permutation[i]; // or int n = i;
int type;
double rnd = JMetalRandom.NextDouble();
// STEP 2.1. Mating selection based on probability
if (rnd < delta) // if (rnd < realb)
{
type = 1; // neighborhood
}
else
{
type = 2; // whole population
}
List <int> p = new List <int>();
MatingSelection(p, n, 2, type);
parents[0] = population.Get(p[0]);
parents[1] = population.Get(p[1]);
//obtain parents
Solution[] offSpring = (Solution[])crossover[1].Execute(parents);
//Solution child;
mutation.Execute(offSpring[0]);
mutation.Execute(offSpring[1]);
/*if(rnd < 0.5)
* {
* child = offSpring[0];
* }
* else
* {
* child = offSpring[1];
* }*/
problem.Evaluate(offSpring[0]);
problem.Evaluate(offSpring[1]);
problem.EvaluateConstraints(offSpring[0]);
problem.EvaluateConstraints(offSpring[1]);
offspringPopulation.Add(offSpring[0]);
offspringPopulation.Add(offSpring[1]);
evaluations += 2;
// STEP 2.3. Repair. Not necessary
// STEP 2.4. Update z_
UpdateReference(offSpring[0]);
UpdateReference(offSpring[1]);
// STEP 2.5. Update of solutions
UpdateProblem(offSpring[0], n, type);
UpdateProblem(offSpring[1], n, type);
}
public SolutionSet Mix()
{
QualityIndicator indicators = new QualityIndicator(problem, fileRead.Qi); // QualityIndicator object
int requiredEvaluations = 0; // Use in the example of use of the
// indicators object (see below)
evaluations = 0;
iteration = 0;
populationSize = int.Parse(fileRead.Ps);
iterationsNumber = int.Parse(fileRead.Itn);
dataDirectory = "Data/Parameters/Weight";
Logger.Log.Info("POPSIZE: " + populationSize);
Console.WriteLine("POPSIZE: " + populationSize);
population = new SolutionSet(populationSize);
indArray = new Solution[problem.NumberOfObjectives];
t = int.Parse(fileRead.T);
nr = int.Parse(fileRead.Nr);
delta = double.Parse(fileRead.Delta);
gamma = double.Parse(fileRead.Gamma);
neighborhood = new int[populationSize][];
for (int i = 0; i < populationSize; i++)
{
neighborhood[i] = new int[t];
}
z = new double[problem.NumberOfObjectives];
//znad = new double[Problem.NumberOfObjectives];
lambda = new double[populationSize][];
for (int i = 0; i < populationSize; i++)
{
lambda[i] = new double[problem.NumberOfObjectives];
}
/*string dir = "Result/" + fileRead.Al + "_" + fileRead.Co + "_" + fileRead.Co2 + "/" + fileRead.Pb + "_" + fileRead.St + "/Record/SBX(" + double.Parse(fileRead.Ra).ToString("#0.00") + ")+ACOR(" + (1-double.Parse(fileRead.Ra)).ToString("#0.00") + ")";
* if (Directory.Exists(dir))
* {
* Console.WriteLine("The directory {0} already exists.", dir);
* }
* else
* {
* Directory.CreateDirectory(dir);
* Console.WriteLine("The directory {0} was created.", dir);
* }*/
//Step 1. Initialization
//Step 1.1 Compute euclidean distances between weight vectors and find T
InitUniformWeight();
InitNeighborhood();
//Step 1.2 Initialize population
InitPoputalion();
//Step 1.3 Initizlize z
InitIdealPoint();
//Step 2 Update
for (int a = 0; a < iterationsNumber; a++)
{
int[] permutation = new int[populationSize];
JMetalCSharp.Metaheuristics.MOEAD.Utils.RandomPermutation(permutation, populationSize);
Solution[] parents = new Solution[2];
int t = 0;
if (a >= double.Parse(fileRead.Ra) * iterationsNumber)
{
for (int i = 0; i < populationSize; i++)
{
int n = permutation[i];
// or int n = i;
int type;
double rnd = JMetalRandom.NextDouble();
// STEP 2.1. ACOR selection based on probability
if (rnd < gamma) // if (rnd < realb)
{
type = 1; // minmum
//parents[0] = population.Get(ACOrSelection2(n, type, pro_T));
}
else
{
type = 2; // whole neighborhood probability
//parents[0] = population.Get(ACOrSelection2(n, type, pro_A));
}
GetStdDev(neighborhood);
//GetStdDev1(neighborhood, type);
//List<int> p = new List<int>();
//MatingSelection(p, n, 1, type);
// STEP 2.2. Reproduction
Solution child;
parents[0] = population.Get(ACOrSelection(n, type));
parents[1] = population.Get(n);
//parents[0] = population.Get(p[0]);
// Apply ACOR crossover
child = (Solution)crossover2.Execute(parents);
child.NumberofReplace = t;
// // Apply mutation
mutation.Execute(child);
// Evaluation
problem.Evaluate(child);
evaluations++;
// STEP 2.3. Repair. Not necessary
// STEP 2.4. Update z_
UpdateReference(child);
// STEP 2.5. Update of solutions
t = UpdateProblemWithReplace(child, n, 1);
}
}
else
{
// Create the offSpring solutionSet
SolutionSet offspringPopulation = new SolutionSet(populationSize);
for (int i = 0; i < (populationSize / 2); i++)
{
int n = permutation[i]; // or int n = i;
int type;
double rnd = JMetalRandom.NextDouble();
// STEP 2.1. Mating selection based on probability
if (rnd < delta) // if (rnd < realb)
{
type = 1; // neighborhood
}
else
{
type = 2; // whole population
}
List <int> p = new List <int>();
MatingSelection(p, n, 2, type);
parents[0] = population.Get(p[0]);
parents[1] = population.Get(p[1]);
//obtain parents
Solution[] offSpring = (Solution[])crossover1.Execute(parents);
//Solution child;
mutation.Execute(offSpring[0]);
mutation.Execute(offSpring[1]);
/*if(rnd < 0.5)
* {
* child = offSpring[0];
* }
* else
* {
* child = offSpring[1];
* }*/
problem.Evaluate(offSpring[0]);
problem.Evaluate(offSpring[1]);
problem.EvaluateConstraints(offSpring[0]);
problem.EvaluateConstraints(offSpring[1]);
offspringPopulation.Add(offSpring[0]);
offspringPopulation.Add(offSpring[1]);
evaluations += 2;
// STEP 2.3. Repair. Not necessary
// STEP 2.4. Update z_
UpdateReference(offSpring[0]);
UpdateReference(offSpring[1]);
// STEP 2.5. Update of solutions
UpdateProblem(offSpring[0], n, type);
UpdateProblem(offSpring[1], n, type);
}
//for (int i = 0; i < populationSize; i++)
//{
// int n = permutation[i]; // or int n = i;
// int type;
// double rnd = JMetalRandom.NextDouble();
// // STEP 2.1. Mating selection based on probability
// if (rnd < delta) // if (rnd < realb)
// {
// type = 1; // neighborhood
// }
// else
// {
// type = 2; // whole population
// }
// List<int> p = new List<int>();
// MatingSelection(p, n, 2, type);
// // STEP 2.2. Reproduction
// Solution child;
// Solution[] parent = new Solution[3];
// parent[0] = population.Get(p[0]);
// parent[1] = population.Get(p[1]);
// parent[2] = population.Get(n);
// // Apply DE crossover
// child = (Solution)crossover1.Execute(new object[] { population.Get(n), parent });
// // Apply mutation
// mutation.Execute(child);
// // Evaluation
// problem.Evaluate(child);
// evaluations++;
// // STEP 2.3. Repair. Not necessary
// // STEP 2.4. Update z_
// UpdateReference(child);
// // STEP 2.5. Update of solutions
// UpdateProblem(child, n, type);
//}
}
/*string filevar = dir + "/VAR" + iteration;
* string filefun = dir + "/FUN" + iteration;
* population.PrintVariablesToFile(filevar);
* population.PrintObjectivesToFile(filefun);*/
iteration++;
if ((indicators != null) && (requiredEvaluations == 0))
{
double HV = indicators.GetHypervolume(population);
if (HV >= (0.98 * indicators.TrueParetoFrontHypervolume))
{
requiredEvaluations = evaluations;
}
}
}
Logger.Log.Info("ITERATION: " + iteration);
Console.WriteLine("ITERATION: " + iteration);
SolutionSet Result = population;
//return population;
// Return the first non-dominated front
Ranking rank = new Ranking(population);
//SolutionSet Result = rank.GetSubfront(0);
return(Result);
}
private void Button_Click_2(object sender, RoutedEventArgs e)
{
FileRead read = new FileRead();
read.fileread();
Problem problem = read.GetProblem();
Algorithm algorithm = read.GetAlgorithm();
QualityIndicator indicators = new QualityIndicator(problem, read.Qi);
NewAlgorithmTest test = new NewAlgorithmTest();
for (int i = 1; i <= int.Parse(read.Rt); i++)
{
// Logger object and file to store log messages
var logger = Logger.Log;
var appenders = logger.Logger.Repository.GetAppenders();
var fileAppender = appenders[0] as log4net.Appender.FileAppender;
fileAppender.File = read.DirPath + "/" + read.Al + i + ".log";
fileAppender.ActivateOptions();
string filevar = read.DirPath + "/VAR" + i;
string filefun = read.DirPath + "/FUN" + i;
FileStream file = new FileStream(read.DirPath + "/MOEADnew" + i + ".txt", FileMode.OpenOrCreate, FileAccess.ReadWrite);
StreamWriter newlogger = new StreamWriter(file);
// Execute the Algorithm
long initTime = Environment.TickCount;
SolutionSet population = test.Mix();
long estimatedTime = Environment.TickCount - initTime;
// Result messages
logger.Info("Total execution time: " + estimatedTime + "ms");
logger.Info("Variables values have been writen to file " + filevar);
newlogger.WriteLine("Total execution time: " + estimatedTime + "ms" + "\n");
newlogger.WriteLine("Variables values have been writen to file " + filevar + "\n");
population.PrintVariablesToFile(filevar);
logger.Info("Objectives values have been writen to file " + filefun);
population.PrintObjectivesToFile(filefun);
Console.WriteLine("Time: " + estimatedTime);
newlogger.WriteLine("Time: " + estimatedTime + "\n");
Console.ReadLine();
if (indicators != null)
{
logger.Info("Quality indicators");
logger.Info("Hypervolume: " + indicators.GetHypervolume(population));
logger.Info("GD : " + indicators.GetGD(population));
logger.Info("IGD : " + indicators.GetIGD(population));
logger.Info("Spread : " + indicators.GetSpread(population));
logger.Info("Epsilon : " + indicators.GetEpsilon(population));
newlogger.WriteLine("Quality indicators");
newlogger.WriteLine("Hypervolume: " + indicators.GetHypervolume(population).ToString("F18") + "\n");
newlogger.WriteLine("GD : " + indicators.GetGD(population).ToString("F18") + "\n");
newlogger.WriteLine("IGD : " + indicators.GetIGD(population).ToString("F18") + "\n");
newlogger.WriteLine("Spread : " + indicators.GetSpread(population).ToString("F18") + "\n");
newlogger.WriteLine("Epsilon : " + indicators.GetEpsilon(population).ToString("F18") + "\n");
int evaluations = test.GetEvaluations();
logger.Info("Speed : " + evaluations + " evaluations");
newlogger.WriteLine("Speed : " + evaluations + " evaluations" + "\n");
}
newlogger.Close();
file.Close();
}
}
public void MyAlgorithm()
{
Problem problem = null; // The problem to solve
Algorithm algorithm = null; // The algorithm to use
Operator crossover = null; // Crossover operator
Operator mutation = null; // Mutation operator
Operator selection = null; // Selection operator
QualityIndicator indicators; // Object to get quality indicators
FileRead fileRead = new FileRead();
fileRead.fileread();
problem = fileRead.GetProblem();
algorithm = fileRead.GetAlgorithm();
//crossover = fileRead.GetCrossover();
mutation = fileRead.GetMutation();
selection = fileRead.GetSelection();
// Quality Indicators Operator
//indicators = new QualityIndicator(problem, "DTLZ1.3D.pf");
indicators = new QualityIndicator(problem, fileRead.Qi);
//indicators = new QualityIndicator(problem, "LZ09_F1.pf");
// Add the operators to the algorithm
algorithm.AddOperator("crossover", crossover);
algorithm.AddOperator("mutation", mutation);
algorithm.AddOperator("selection", selection);
// Add the indicator object to the algorithm
algorithm.SetInputParameter("indicators", indicators);
for (int i = 1; i <= int.Parse(fileRead.Rt); i++)
{
// Logger object and file to store log messages
var logger = Logger.Log;
var appenders = logger.Logger.Repository.GetAppenders();
var fileAppender = appenders[0] as log4net.Appender.FileAppender;
fileAppender.File = fileRead.DirPath + "/" + fileRead.Al + i + ".log";
fileAppender.ActivateOptions();
string filevar = fileRead.DirPath + "/VAR" + i;
string filefun = fileRead.DirPath + "/FUN" + i;
FileStream file = new FileStream(fileRead.DirPath + "/" + fileRead.Al + "new" + i + ".txt", FileMode.OpenOrCreate, FileAccess.ReadWrite);
StreamWriter newlogger = new StreamWriter(file);
// Execute the Algorithm
long initTime = Environment.TickCount;
SolutionSet population = algorithm.Execute();
long estimatedTime = Environment.TickCount - initTime;
// Result messages
logger.Info("Total execution time: " + estimatedTime + "ms");
logger.Info("Variables values have been writen to file " + filevar);
newlogger.WriteLine("Total execution time: " + estimatedTime + "ms" + "\n");
newlogger.WriteLine("Variables values have been writen to file " + filevar + "\n");
population.PrintVariablesToFile(filevar);
logger.Info("Objectives values have been writen to file " + filefun);
population.PrintObjectivesToFile(filefun);
Console.WriteLine("Time: " + estimatedTime);
newlogger.WriteLine("Time: " + estimatedTime + "\n");
Console.ReadLine();
if (indicators != null)
{
logger.Info("Quality indicators");
logger.Info("Hypervolume: " + indicators.GetHypervolume(population));
logger.Info("GD : " + indicators.GetGD(population));
logger.Info("IGD : " + indicators.GetIGD(population));
logger.Info("Spread : " + indicators.GetSpread(population));
logger.Info("Epsilon : " + indicators.GetEpsilon(population));
newlogger.WriteLine("Quality indicators");
newlogger.WriteLine("Hypervolume: " + indicators.GetHypervolume(population).ToString("F18") + "\n");
newlogger.WriteLine("GD : " + indicators.GetGD(population).ToString("F18") + "\n");
newlogger.WriteLine("IGD : " + indicators.GetIGD(population).ToString("F18") + "\n");
newlogger.WriteLine("Spread : " + indicators.GetSpread(population).ToString("F18") + "\n");
newlogger.WriteLine("Epsilon : " + indicators.GetEpsilon(population).ToString("F18") + "\n");
int evaluations = (int)algorithm.GetOutputParameter("evaluations");
logger.Info("Speed : " + evaluations + " evaluations");
newlogger.WriteLine("Speed : " + evaluations + " evaluations" + "\n");
}
newlogger.Close();
file.Close();
algorithm.AddOperator("mutation", mutation);
}
}
/// <summary>
/// Usage: three options
/// - NSGAIIAdaptive
/// - NSGAIIAdaptive problemName
/// - NSGAIIAdaptive problemName paretoFrontFile
/// </summary>
/// <param name="args">Command line arguments.</param>
public static void Main(string[] args)
{
Problem problem; // The problem to solve
Algorithm algorithm; // The algorithm to use
Operator selection; // Selection operator
Dictionary <string, object> parameters; // Operator parameters
QualityIndicator indicators; // Object to get quality indicators
// Logger object and file to store log messages
var logger = Logger.Log;
var appenders = logger.Logger.Repository.GetAppenders();
var fileAppender = appenders[0] as log4net.Appender.FileAppender;
fileAppender.File = "NSGAIIAdaptive.log";
fileAppender.ActivateOptions();
indicators = null;
if (args.Length == 1)
{
object[] param = { "Real" };
problem = ProblemFactory.GetProblem(args[0], param);
}
else if (args.Length == 2)
{
object[] param = { "Real" };
problem = ProblemFactory.GetProblem(args[0], param);
indicators = new QualityIndicator(problem, args[1]);
}
else
{ // Default problem
problem = new Kursawe("Real", 3);
//problem = new Kursawe("BinaryReal", 3);
//problem = new Water("Real");
//problem = new ZDT1("ArrayReal", 100);
//problem = new ConstrEx("Real");
//problem = new DTLZ1("Real");
//problem = new OKA2("Real") ;
}
problem = new LZ09_F3("Real");
algorithm = new JMetalCSharp.Metaheuristics.NSGAII.NSGAIIAdaptive(problem);
//algorithm = new ssNSGAIIAdaptive(problem);
// Algorithm parameters
algorithm.SetInputParameter("populationSize", 100);
algorithm.SetInputParameter("maxEvaluations", 150000);
// Selection Operator
parameters = null;
selection = SelectionFactory.GetSelectionOperator("BinaryTournament2", parameters);
// Add the operators to the algorithm
algorithm.AddOperator("selection", selection);
// Add the indicator object to the algorithm
algorithm.SetInputParameter("indicators", indicators);
Offspring[] getOffspring = new Offspring[3];
double CR, F;
CR = 1.0;
F = 0.5;
getOffspring[0] = new DifferentialEvolutionOffspring(CR, F);
getOffspring[1] = new SBXCrossoverOffspring(1.0, 20);
//getOffspring[1] = new BLXAlphaCrossoverOffspring(1.0, 0.5);
getOffspring[2] = new PolynomialMutationOffspring(1.0 / problem.NumberOfVariables, 20);
//getOffspring[2] = new NonUniformMutationOffspring(1.0/problem.getNumberOfVariables(), 0.5, 150000);
algorithm.SetInputParameter("offspringsCreators", getOffspring);
// Execute the Algorithm
long initTime = Environment.TickCount;
SolutionSet population = algorithm.Execute();
long estimatedTime = Environment.TickCount - initTime;
// Result messages
logger.Info("Total execution time: " + estimatedTime + "ms");
logger.Info("Variables values have been writen to file VAR");
population.PrintVariablesToFile("VAR");
logger.Info("Objectives values have been writen to file FUN");
population.PrintObjectivesToFile("FUN");
Console.WriteLine("Time: " + estimatedTime);
Console.ReadLine();
if (indicators != null)
{
logger.Info("Quality indicators");
logger.Info("Hypervolume: " + indicators.GetHypervolume(population));
logger.Info("GD : " + indicators.GetGD(population));
logger.Info("IGD : " + indicators.GetIGD(population));
logger.Info("Spread : " + indicators.GetSpread(population));
logger.Info("Epsilon : " + indicators.GetEpsilon(population));
}
}
/// <summary>
/// Runs the NSGA-II algorithm.
/// </summary>
/// <returns>a <code>SolutionSet</code> that is a set of non dominated solutions as a result of the algorithm execution</returns>
public override SolutionSet Execute()
{
int populationSize = -1;
int maxEvaluations = -1;
int evaluations;
QualityIndicator indicators = null; // QualityIndicator object
int requiredEvaluations; // Use in the example of use of the
// indicators object (see below)
SolutionSet population;
SolutionSet offspringPopulation;
SolutionSet union;
Operator mutationOperator;
Operator crossoverOperator;
Operator selectionOperator;
Distance distance = new Distance();
//Read the parameters
JMetalCSharp.Utils.Utils.GetIntValueFromParameter(this.InputParameters, "maxEvaluations", ref maxEvaluations);
JMetalCSharp.Utils.Utils.GetIntValueFromParameter(this.InputParameters, "populationSize", ref populationSize);
JMetalCSharp.Utils.Utils.GetIndicatorsFromParameters(this.InputParameters, "indicators", ref indicators);
//Initialize the variables
population = new SolutionSet(populationSize);
evaluations = 0;
requiredEvaluations = 0;
//Read the operators
mutationOperator = Operators["mutation"];
crossoverOperator = Operators["crossover"];
selectionOperator = Operators["selection"];
var plotCounter = 0;
var plotModulo = 4;
Random random = new Random(2);
JMetalRandom.SetRandom(random);
// Create the initial solutionSet
IntergenSolution newSolution;
for (int i = 0; i < populationSize; i++)
{
//var test = (IntergenProblem) Problem;
newSolution = new IntergenSolution((IntergenProblem)Problem);
Problem.Evaluate(newSolution);
Problem.EvaluateConstraints(newSolution);
evaluations++;
population.Add(newSolution);
}
// Generations
while (evaluations < maxEvaluations)
{
// Create the offSpring solutionSet
offspringPopulation = new SolutionSet(populationSize);
IntergenSolution[] parents = new IntergenSolution[2];
for (int i = 0; i < (populationSize / 2); i++)
{
if (evaluations < maxEvaluations)
{
//obtain parents
parents[0] = (IntergenSolution)selectionOperator.Execute(population);
parents[1] = (IntergenSolution)selectionOperator.Execute(population);
IntergenSolution[] offSpring = (IntergenSolution[])crossoverOperator.Execute(parents);
mutationOperator.Execute(offSpring[0]);
mutationOperator.Execute(offSpring[1]);
Problem.Evaluate(offSpring[0]);
Problem.EvaluateConstraints(offSpring[0]);
Problem.Evaluate(offSpring[1]);
Problem.EvaluateConstraints(offSpring[1]);
offspringPopulation.Add(offSpring[0]);
offspringPopulation.Add(offSpring[1]);
evaluations += 2;
}
}
// Create the solutionSet union of solutionSet and offSpring
union = ((SolutionSet)population).Union(offspringPopulation);
// Ranking the union
Ranking ranking = new Ranking(union);
int remain = populationSize;
int index = 0;
SolutionSet front = null;
population.Clear();
// Obtain the next front
front = ranking.GetSubfront(index);
while ((remain > 0) && (remain >= front.Size()))
{
//Assign crowding distance to individuals
distance.CrowdingDistanceAssignment(front, Problem.NumberOfObjectives);
//Add the individuals of this front
for (int k = 0; k < front.Size(); k++)
{
population.Add(front.Get(k));
}
//Decrement remain
remain = remain - front.Size();
//Obtain the next front
index++;
if (remain > 0)
{
front = ranking.GetSubfront(index);
}
}
// Remain is less than front(index).size, insert only the best one
if (remain > 0)
{ // front contains individuals to insert
distance.CrowdingDistanceAssignment(front, Problem.NumberOfObjectives);
front.Sort(new CrowdingComparator());
for (int k = 0; k < remain; k++)
{
population.Add(front.Get(k));
}
remain = 0;
}
// This piece of code shows how to use the indicator object into the code
// of NSGA-II. In particular, it finds the number of evaluations required
// by the algorithm to obtain a Pareto front with a hypervolume higher
// than the hypervolume of the true Pareto front.
if ((indicators != null) && (requiredEvaluations == 0))
{
double HV = indicators.GetHypervolume(population);
if (HV >= (0.98 * indicators.TrueParetoFrontHypervolume))
{
requiredEvaluations = evaluations;
}
}
var sol0 = front.Best(new CrowdingComparator());
//if (plotCounter%plotModulo == 0)
SolutionPlotter.Plot(sol0);
ProgressReporter.ReportSolution(evaluations, sol0, _worker);
}
// Return as output parameter the required evaluations
SetOutputParameter("evaluations", requiredEvaluations);
// Return the first non-dominated front
Ranking rank = new Ranking(population);
Result = rank.GetSubfront(0);
return(Result);
}
/// <summary>
///Usage: three choices
/// - GDE3
/// - GDE3 problemName
/// - GDE3 problemName paretoFrontFile
/// </summary>
/// <param name="args">Command line arguments.</param>
public static void Main(string[] args)
{
Problem problem; // The problem to solve
Algorithm algorithm; // The algorithm to use
Operator selection;
Operator crossover;
Dictionary <string, object> parameters; // Operator parameters
QualityIndicator indicators; // Object to get quality indicators
// Logger object and file to store log messages
var logger = Logger.Log;
var appenders = logger.Logger.Repository.GetAppenders();
var fileAppender = appenders[0] as log4net.Appender.FileAppender;
fileAppender.File = "GDE3.log";
fileAppender.ActivateOptions();
indicators = null;
if (args.Length == 1)
{
object[] param = { "Real" };
problem = ProblemFactory.GetProblem(args[0], param);
}
else if (args.Length == 2)
{
object[] param = { "Real" };
problem = ProblemFactory.GetProblem(args[0], param);
indicators = new QualityIndicator(problem, args[1]);
}
else
{ // Default problem
problem = new Kursawe("Real", 3);
//problem = new Water("Real");
//problem = new ZDT1("ArrayReal", 100);
//problem = new ConstrEx("Real");
//problem = new DTLZ1("Real");
//problem = new OKA2("Real") ;
}
algorithm = new JMetalCSharp.Metaheuristics.GDE3.GDE3(problem);
// Algorithm parameters
algorithm.SetInputParameter("populationSize", 100);
algorithm.SetInputParameter("maxIterations", 250);
// Crossover operator
parameters = new Dictionary <string, object>();
parameters.Add("CR", 0.5);
parameters.Add("F", 0.5);
crossover = CrossoverFactory.GetCrossoverOperator("DifferentialEvolutionCrossover", parameters);
// Add the operators to the algorithm
parameters = null;
selection = SelectionFactory.GetSelectionOperator("DifferentialEvolutionSelection", parameters);
algorithm.AddOperator("crossover", crossover);
algorithm.AddOperator("selection", selection);
// Execute the Algorithm
long initTime = Environment.TickCount;
SolutionSet population = algorithm.Execute();
long estimatedTime = Environment.TickCount - initTime;
// Result messages
logger.Info("Total execution time: " + estimatedTime + "ms");
logger.Info("Objectives values have been writen to file FUN");
population.PrintObjectivesToFile("FUN");
logger.Info("Variables values have been writen to file VAR");
population.PrintVariablesToFile("VAR");
if (indicators != null)
{
logger.Info("Quality indicators");
logger.Info("Hypervolume: " + indicators.GetHypervolume(population));
logger.Info("GD : " + indicators.GetGD(population));
logger.Info("IGD : " + indicators.GetIGD(population));
logger.Info("Spread : " + indicators.GetSpread(population));
logger.Info("Epsilon : " + indicators.GetEpsilon(population));
}
Console.WriteLine("Total execution time gde: moead" + estimatedTime + "ms");
Console.ReadLine();
}
/// <summary>
/// Usage: three options
/// - PMOEAD
/// - PMOEAD problemName
/// - PMOEAD problemName ParetoFrontFile
/// - PMOEAD problemName numberOfThreads dataDirectory
/// </summary>
/// <param name="args">Command line arguments. The first (optional) argument specifies the problem to solve.</param>
public static void Main(string[] args)
{
Problem problem; // The problem to solve
Algorithm algorithm; // The algorithm to use
Operator crossover; // Crossover operator
Operator mutation; // Mutation operator
QualityIndicator indicators; // Object to get quality indicators
Dictionary <string, object> parameters; // Operator parameters
int numberOfThreads = 4;
string dataDirectory = "";
// Logger object and file to store log messages
var logger = Logger.Log;
var appenders = logger.Logger.Repository.GetAppenders();
var fileAppender = appenders[0] as log4net.Appender.FileAppender;
fileAppender.File = "PMOEAD.log";
fileAppender.ActivateOptions();
indicators = null;
if (args.Length == 1)
{ // args[0] = problem name
object[] paramsList = { "Real" };
problem = ProblemFactory.GetProblem(args[0], paramsList);
}
else if (args.Length == 2)
{ // args[0] = problem name, [1] = pareto front file
object[] paramsList = { "Real" };
problem = ProblemFactory.GetProblem(args[0], paramsList);
indicators = new QualityIndicator(problem, args[1]);
}
else if (args.Length == 3)
{ // args[0] = problem name, [1] = threads, [2] = data directory
object[] paramsList = { "Real" };
problem = ProblemFactory.GetProblem(args[0], paramsList);
numberOfThreads = int.Parse(args[1]);
dataDirectory = args[2];
}
else
{ // Problem + number of threads + data directory
problem = new Kursawe("Real", 3);
//problem = new Kursawe("BinaryReal", 3);
//problem = new Water("Real");
//problem = new ZDT1("ArrayReal", 100);
//problem = new ConstrEx("Real");
//problem = new DTLZ1("Real");
//problem = new OKA2("Real") ;
}
algorithm = new JMetalCSharp.Metaheuristics.MOEAD.PMOEAD(problem);
// Algorithm parameters
algorithm.SetInputParameter("populationSize", 300);
algorithm.SetInputParameter("maxEvaluations", 150000);
algorithm.SetInputParameter("numberOfThreads", numberOfThreads);
// Directory with the files containing the weight vectors used in
// Q. Zhang, W. Liu, and H Li, The Performance of a New Version of MOEA/D
// on CEC09 Unconstrained MOP Test Instances Working Report CES-491, School
// of CS & EE, University of Essex, 02/2009.
// http://dces.essex.ac.uk/staff/qzhang/MOEAcompetition/CEC09final/code/ZhangMOEADcode/moead0305.rar
algorithm.SetInputParameter("dataDirectory", "Data/Parameters/Weight");
algorithm.SetInputParameter("T", 20);
algorithm.SetInputParameter("delta", 0.9);
algorithm.SetInputParameter("nr", 2);
// Crossover operator
parameters = new Dictionary <string, object>();
parameters.Add("CR", 1.0);
parameters.Add("F", 0.5);
crossover = CrossoverFactory.GetCrossoverOperator("DifferentialEvolutionCrossover", parameters);
// Mutation operator
parameters = new Dictionary <string, object>();
parameters.Add("probability", 1.0 / problem.NumberOfVariables);
parameters.Add("distributionIndex", 20.0);
mutation = MutationFactory.GetMutationOperator("PolynomialMutation", parameters);
algorithm.AddOperator("crossover", crossover);
algorithm.AddOperator("mutation", mutation);
// Execute the Algorithm
long initTime = Environment.TickCount;
SolutionSet population = algorithm.Execute();
long estimatedTime = Environment.TickCount - initTime;
// Result messages
logger.Info("Total execution time: " + estimatedTime + " ms");
logger.Info("Objectives values have been writen to file FUN");
population.PrintObjectivesToFile("FUN");
logger.Info("Variables values have been writen to file VAR");
population.PrintVariablesToFile("VAR");
Console.ReadLine();
if (indicators != null)
{
logger.Info("Quality indicators");
logger.Info("Hypervolume: " + indicators.GetHypervolume(population));
logger.Info("GD : " + indicators.GetGD(population));
logger.Info("IGD : " + indicators.GetIGD(population));
logger.Info("Spread : " + indicators.GetSpread(population));
}
}
public void UpdateQualityIndicator(QualityIndicator currentQuality)
{
if (_lastQuality != currentQuality)
{
var currentIndicator = "MediumIndicator";
var lastIndicator = "MediumIndicator";
switch (_lastQuality)
{
case QualityIndicator.ToBad:
lastIndicator = "BadIndicator";
break;
case QualityIndicator.VeryPoor:
case QualityIndicator.Poor:
lastIndicator = "PoorIndicator";
break;
case QualityIndicator.Medium:
lastIndicator = "MediumIndicator";
break;
case QualityIndicator.Good:
lastIndicator = "GoodIndicator";
break;
default:
lastIndicator = "None";
break;
}
switch (currentQuality)
{
case QualityIndicator.ToBad:
currentIndicator = "BadIndicator";
break;
case QualityIndicator.VeryPoor:
case QualityIndicator.Poor:
currentIndicator = "PoorIndicator";
break;
case QualityIndicator.Medium:
currentIndicator = "MediumIndicator";
break;
case QualityIndicator.Good:
currentIndicator = "GoodIndicator";
break;
default:
return;
}
_lastQuality = currentQuality;
var container =
FindChild <Grid>(qualityIndicatoWindow.TransparentWindow, "QualityContainer");
if (container != null)
{
var image =
FindChild <Image>(container, lastIndicator);
if (image != null)
{
image.Visibility = Visibility.Collapsed;
}
else
{
image =
FindChild <Image>(container, "BadIndicator");
if (image != null)
{
image.Visibility = Visibility.Collapsed;
}
image =
FindChild <Image>(container, "PoorIndicator");
if (image != null)
{
image.Visibility = Visibility.Collapsed;
}
image =
FindChild <Image>(container, "MediumIndicator");
if (image != null)
{
image.Visibility = Visibility.Collapsed;
}
image =
FindChild <Image>(container, "GoodIndicator");
if (image != null)
{
image.Visibility = Visibility.Collapsed;
}
}
image =
FindChild <Image>(container, currentIndicator);
if (image != null)
{
image.Visibility = Visibility.Visible;
}
}
}
qualityIndicatoWindow.Refresh();
qualityIndicatoWindow.UpdateWindow();
}
public ActionResult ProviderTrafficLightStatus(int id = 0, bool forceStop = false)
{
const string mutexKey = "ProviderTrafficLightStatusMutex";
var mutex = (bool?)CacheManagement.CacheHandler.Get(mutexKey);
if (mutex.HasValue && mutex.Value)
{
if (forceStop)
{
CacheManagement.CacheHandler.Invalidate(mutexKey);
}
Response.Write(forceStop
? "Job in progress, stopped..."
: "Job in progress, skipping...");
return(null);
}
if (forceStop)
{
Response.Write("Not in progress...");
return(null);
}
CacheManagement.CacheHandler.Add(mutexKey, true, new TimeSpan(12, 0, 0));
var model = new ProviderTrafficLightStatusViewModel();
var providers = GetProviderTrafficLightStatusInfo();
var sendToList = new Dictionary <int, ProviderTrafficLightStatusViewModel.ProviderTrafficLightEmail>();
var today = Request.IsAuthenticated && userContext.IsAdministration()
? DateTime.UtcNow.AddDays(id).Date
: DateTime.UtcNow.Date;
model.Today = today;
foreach (var item in providers)
{
var lastUpdate = item.ModifiedDateTimeUtc == null ? (DateTime?)null : item.ModifiedDateTimeUtc.Value.Date;
var lastEmail = item.LastEmailDateTimeUtc;
var action = model.Skipped;
if (item.SFAFunded /* and it doesn't matter if they are also DfE funded, SFA emails trump the DfE ones */)
{
// Number of months the provider stays green after updating
var greenPeriod = lastUpdate == null
? 0
: QualityIndicator.GetGreenDuration(lastUpdate.Value.Month);
// Offset for when they go amber (alwas one month before going red)
var amberPeriod = greenPeriod + 1;
if (lastEmail == today)
{
action = model.EmailAlreadySent;
}
else if (lastUpdate == null)
{
action = model.Skipped;
item.EmailTemplateId = null;
item.NextEmailTemplateId = Constants.EmailTemplates.SfaProviderTrafficLightIsNowRed;
item.NextEmailDateTimeUtc = item.ProviderCreatedDateTimeUtc.AddMonths(NewProviderGracePeriod).Date;
item.TrafficLightStatusId = QualityIndicator.TrafficLight.Red;
}
else if (today < lastUpdate.Value.AddMonths(greenPeriod))
{
action = model.Skipped;
item.NextEmailTemplateId = Constants.EmailTemplates.SfaProviderTrafficLightIsNowAmber;
item.NextEmailDateTimeUtc = lastUpdate.Value.AddMonths(greenPeriod);
item.TrafficLightStatusId = QualityIndicator.TrafficLight.Green;
}
else if (today == lastUpdate.Value.AddMonths(greenPeriod))
{
action = model.AmberToday;
item.EmailTemplateId = Constants.EmailTemplates.SfaProviderTrafficLightIsNowAmber;
item.NextEmailTemplateId = Constants.EmailTemplates.SfaProviderTrafficLightIsAmberWeek1;
item.NextEmailDateTimeUtc = lastUpdate.Value.AddMonths(greenPeriod).AddDays(7);
item.TrafficLightStatusId = QualityIndicator.TrafficLight.Amber;
}
else if (today == lastUpdate.Value.AddMonths(greenPeriod).AddDays(7))
{
action = model.AmberForOneWeek;
item.EmailTemplateId = Constants.EmailTemplates.SfaProviderTrafficLightIsAmberWeek1;
item.NextEmailTemplateId = Constants.EmailTemplates.SfaProviderStatusRedInOneWeek;
item.NextEmailDateTimeUtc = lastUpdate.Value.AddMonths(amberPeriod).AddDays(-7);
item.TrafficLightStatusId = QualityIndicator.TrafficLight.Amber;
}
else if (today == lastUpdate.Value.AddMonths(amberPeriod).AddDays(-7))
{
action = model.RedInOneWeek;
item.EmailTemplateId = Constants.EmailTemplates.SfaProviderStatusRedInOneWeek;
item.NextEmailTemplateId = Constants.EmailTemplates.SfaProviderTrafficLightIsNowRed;
item.NextEmailDateTimeUtc = lastUpdate.Value.AddMonths(amberPeriod);
item.TrafficLightStatusId = QualityIndicator.TrafficLight.Amber;
}
else if (today == lastUpdate.Value.AddMonths(amberPeriod))
{
action = model.RedToday;
item.EmailTemplateId = Constants.EmailTemplates.SfaProviderTrafficLightIsNowRed;
item.NextEmailTemplateId = Constants.EmailTemplates.SfaProviderTrafficLightIsRedWeeklyReminder;
item.NextEmailDateTimeUtc = lastUpdate.Value.AddMonths(amberPeriod).AddDays(7);
item.TrafficLightStatusId = QualityIndicator.TrafficLight.Red;
}
else if (today > lastUpdate.Value.AddMonths(amberPeriod) &&
lastUpdate.Value.AddMonths(amberPeriod).DayOfWeek == today.DayOfWeek)
{
action = model.StillRed;
item.EmailTemplateId = Constants.EmailTemplates.SfaProviderTrafficLightIsRedWeeklyReminder;
item.NextEmailTemplateId = Constants.EmailTemplates.SfaProviderTrafficLightIsRedWeeklyReminder;
item.NextEmailDateTimeUtc = today.AddDays(7);
item.TrafficLightStatusId = QualityIndicator.TrafficLight.Red;
}
}
else if (item.DFE1619Funded && lastUpdate == null)
{
var notifyDate =
item.ProviderCreatedDateTimeUtc.Month < 11
? new DateTime(today.Year, 11, 1)
: new DateTime(today.AddYears(1).Year, 11, 1);
action = model.Skipped;
item.EmailTemplateId = null;
item.NextEmailTemplateId = Constants.EmailTemplates.Dfe1619ProviderTrafficLightIsNowRed;
item.NextEmailDateTimeUtc = notifyDate;
item.TrafficLightStatusId = QualityIndicator.TrafficLight.Red;
}
else if (item.DFE1619Funded && lastUpdate.HasValue)
{
item.TrafficLightStatusId = QualityIndicator.DfeProviderDateToIndex(lastUpdate.Value);
var greenEndDate = QualityIndicator.GetDfeProviderGreenEndDate(lastUpdate.Value).Date;
switch (item.TrafficLightStatusId)
{
case QualityIndicator.TrafficLight.Green:
action = model.Skipped;
item.NextEmailTemplateId = Constants.EmailTemplates.Dfe1619ProviderTrafficLightIsNowAmber;
item.NextEmailDateTimeUtc = greenEndDate.AddDays(1);
break;
case QualityIndicator.TrafficLight.Amber:
if (today.Month == 10 && today.Day == 1)
{
action = model.AmberToday;
item.EmailTemplateId = Constants.EmailTemplates.Dfe1619ProviderTrafficLightIsNowAmber;
item.NextEmailTemplateId = Constants.EmailTemplates.Dfe1619ProviderTrafficLightIsAmberWeek1;
item.NextEmailDateTimeUtc = today.AddDays(7);
}
else if (today.Month == 10 && today.Day == 8)
{
action = model.AmberForOneWeek;
item.EmailTemplateId = Constants.EmailTemplates.Dfe1619ProviderTrafficLightIsAmberWeek1;
item.NextEmailTemplateId = Constants.EmailTemplates.Dfe1619ProviderStatusRedInOneWeek;
item.NextEmailDateTimeUtc = today.AddDays(17);
}
break;
case QualityIndicator.TrafficLight.Red:
if (today.Month == 10 && today.Day == 25 && today.Year == greenEndDate.Year)
{
action = model.RedInOneWeek;
item.EmailTemplateId = Constants.EmailTemplates.Dfe1619ProviderStatusRedInOneWeek;
item.NextEmailTemplateId = Constants.EmailTemplates.Dfe1619ProviderTrafficLightIsNowRed;
item.NextEmailDateTimeUtc = today.AddDays(7);
}
else if (today.Month == 11 && today.Day == 1 && today.Year == greenEndDate.Year)
{
action = model.RedToday;
item.EmailTemplateId = Constants.EmailTemplates.Dfe1619ProviderTrafficLightIsNowRed;
item.NextEmailTemplateId =
Constants.EmailTemplates.Dfe1619ProviderTrafficLightIsRedWeeklyReminder;
item.NextEmailDateTimeUtc = today.AddDays(7);
}
else if ((int)(today - lastUpdate.Value).TotalDays % 7 == 0)
{
action = model.StillRed;
item.EmailTemplateId = Constants.EmailTemplates.Dfe1619ProviderTrafficLightIsRedWeeklyReminder;
item.NextEmailTemplateId =
Constants.EmailTemplates.Dfe1619ProviderTrafficLightIsRedWeeklyReminder;
item.NextEmailDateTimeUtc = today.AddDays(7);
}
break;
}
}
//if (item.EmailTemplateId != null || item.NextEmailTemplateId != null)
//{
item.EmailDateTimeUtc = item.EmailTemplateId == null ? (DateTime?)null : today;
item.HasValidRecipients = false;
sendToList.Add(item.ProviderId, item);
//}
if (item.TrafficLightStatusId == 0)
{
item.TrafficLightStatusId = QualityIndicator.GetTrafficStatus(item.ModifiedDateTimeUtc, item.SFAFunded, item.DFE1619Funded);
}
model.Log[action].Add(item.ProviderName);
}
// Send the emails - this updates the sendToList
SendProviderTrafficLightStatusEmails(sendToList, today);
// Log the results
SaveHistoryLog(sendToList, today);
// Delete mutex
CacheManagement.CacheHandler.Invalidate(mutexKey);
return(View(model));
}
private void SendProviderTrafficLightStatusEmails(Dictionary <int, ProviderTrafficLightStatusViewModel.ProviderTrafficLightEmail> providers, DateTime today)
{
if (!providers.Any())
{
return;
}
var sendToProviderIds = providers.Values
.Where(x => x.EmailTemplateId != null)
.Where(x => !x.QualityEmailsPaused)
.Select(x => x.ProviderId)
.ToList();
var superUsers = ProvisionUtilities.GetProviderUsers(db, sendToProviderIds, false, true);
// Send the emails out
foreach (var user in superUsers)
{
if (!providers.ContainsKey(user.ProviderId))
{
continue;
}
var provider = providers[user.ProviderId];
if (provider.EmailTemplateId == null)
{
continue;
}
provider.HasValidRecipients = true;
//AppGlobal.EmailQueue.AddToSendQueue(
// TemplatedEmail.EmailMessage(
// new MailAddress(user.Email, user.Name),
// null,
// null,
// provider.EmailTemplateId.Value,
// new List<EmailParameter>
// {
// new EmailParameter("%PROVIDERNAME%", provider.ProviderName),
// new EmailParameter("%LASTUPDATEDATE%",
// provider.ModifiedDateTimeUtc.HasValue
// ? provider.ModifiedDateTimeUtc.Value.ToString(
// Constants.ConfigSettings.ShortDateFormat)
// : AppGlobal.Language.GetText(this, "NeverUpdated", "never")),
// new EmailParameter("%MONTHSSINCEUPDATE%",
// provider.ModifiedDateTimeUtc.HasValue
// ? QualityIndicator.GetMonthsBetween(provider.ModifiedDateTimeUtc.Value, DateTime.UtcNow).ToString()
// : NewProviderGracePeriod.ToString(CultureInfo.InvariantCulture))
// }));
var emailMessage = TemplatedEmail.EmailMessage(
new MailAddress(user.Email, user.Name),
null,
null,
provider.EmailTemplateId.Value,
new List <EmailParameter>
{
new EmailParameter("%PROVIDERNAME%", provider.ProviderName),
new EmailParameter("%LASTUPDATEDATE%",
provider.ModifiedDateTimeUtc.HasValue
? provider.ModifiedDateTimeUtc.Value.ToString(
Constants.ConfigSettings.ShortDateFormat)
: AppGlobal.Language.GetText(this, "NeverUpdated", "never")),
new EmailParameter("%MONTHSSINCEUPDATE%",
provider.ModifiedDateTimeUtc.HasValue
? QualityIndicator.GetMonthsBetween(provider.ModifiedDateTimeUtc.Value, DateTime.UtcNow).ToString()
: NewProviderGracePeriod.ToString(CultureInfo.InvariantCulture))
});
var response = SfaSendGridClient.SendGridEmailMessage(emailMessage, null);
}
// Update the providers
foreach (var providerId in sendToProviderIds)
{
var provider = new Provider
{
ProviderId = providerId,
TrafficLightEmailDateTimeUtc = today
};
db.Providers.Attach(provider);
db.Entry(provider).Property(x => x.TrafficLightEmailDateTimeUtc).IsModified = true;
providers[providerId].EmailDateTimeUtc = today;
}
db.Configuration.ValidateOnSaveEnabled = false;
db.SaveChanges();
}
