internal void EvaluateObjective(Solution child1)
{
child1.ObjectiveValue.Clear();
foreach (ObjectiveFunction f in functions)
{
child1.ObjectiveValue.Add(f.Evaluate(child1.DecisionVariables));
}
}
public Solution(Solution sol)
: this()
{
// decisionVariables = new List<double>();
foreach (double s in sol.DecisionVariables)
{
this.decisionVariables.Add(s);
}
}
public List<Solution> Crossover(Solution parent1, Solution parent2)
{
double max = functions.GetUpperThreshold();
double min = functions.GetLowerThreshold();
Solution child1 = new Solution();
Solution child2 = new Solution();
Random rand = new Random();
for (int j = 0; j < functions.GetDecisionVariablesCount(); j++)
{
double c;
double r = rand.NextDouble();
if (r <= 0.5)
{
c = Math.Pow((2 * r), (1 / (mu + 1)));
}
else
{
c = Math.Pow(1 / (2 * r), 1 / (mu + 1));
}
child1.DecisionVariables.Add(0.5 * (((1 + c) * parent1.DecisionVariables[j]) + (1 - c) * parent2.DecisionVariables[j]));
child2.DecisionVariables.Add(0.5 * (((1 - c) * parent1.DecisionVariables[j]) + (1 + c) * parent2.DecisionVariables[j]));
if (child1.DecisionVariables[j] > max)
{
child1.DecisionVariables[j] = max;
}
else
{
if (child1.DecisionVariables[j] < min)
{
child1.DecisionVariables[j] = min;
}
}
if (child2.DecisionVariables[j] > max)
{
child2.DecisionVariables[j] = max;
}
else
{
if (child2.DecisionVariables[j] < min)
{
child2.DecisionVariables[j] = min;
}
}
}
functions.EvaluateObjective(child1);
functions.EvaluateObjective(child2);
List<Solution> newBees = new List<Solution>();
newBees.Add(child1);
newBees.Add(child2);
return newBees;
}
public void AddIndividual(int rank, Solution individual)
{
if (rank >= rankings.Count)
{
rankings.Add(new List<Solution>());
}
try
{
rankings.ElementAt(rank).Add(individual);
}
catch (Exception)
{
throw;
}
}
public List<Solution> Mutation(Solution solution)
{
double max = functions.GetUpperThreshold();
double min = functions.GetLowerThreshold();
Random rand = new Random();
List<Solution> newBees = new List<Solution>();
for (int j = 0; j < functions.GetDecisionVariablesCount(); j++)
{
double random = rand.NextDouble();
double delta;
if (random < 0.5)
{
delta = Math.Pow(2 * random, 1 / (mum + 1)) - 1;
}
else
{
delta = 1 - Math.Pow(2 * (1 - random), 1 / (mum + 1));
}
solution.DecisionVariables[j] += delta;
if (solution.DecisionVariables[j] > max)
{
solution.DecisionVariables[j] = max;
}
else
{
if (solution.DecisionVariables[j] < min)
{
solution.DecisionVariables[j] = min;
}
}
}
functions.EvaluateObjective(solution);
newBees.Add(solution);
return newBees;
}
public void AddDominatedSolution(Solution individual)
{
dominatedSolutions.Add(individual);
}
public void RemoveDominatedSolution(Solution individual)
{
dominatedSolutions.Remove(individual);
}
public bool Dominates(Solution individual)
{
int counter = 0;
bool dominates = false;
for(int i = 0; i < value.Count; i++)
{
if(this.ObjectiveValue[i] <= individual.ObjectiveValue[i])
{
counter++;
}
}
if (counter == value.Count)
{
for (int i = 0; i < value.Count; i++)
{
if (this.value[i] < individual.ObjectiveValue[i])
{
dominates = true;
}
}
}
return dominates;
}
public void Add(Solution individual)
{
this.genom.Add(individual);
}
public void Remove(Solution individual)
{
this.genom.Remove(individual);
}
public void NewPopulation(int count)
{
// should be done
double min = functions.GetLowerThreshold();
double max = functions.GetUpperThreshold();
int n = functions.GetDecisionVariablesCount();
Random rand = new Random();
for (int i = 0; i < count; i++)
{
Solution item = new Solution();
for (int j = 0; j < n; j++)
{
item.AddDecisionVariable(min + (max - min) * rand.NextDouble());
}
this.Add(item);
}
}
