Add single objective optimization algorithm, add method for finding i…

…deal point for given problem

src/core/Problem.java

@@ -2,8 +2,15 @@
 
 import java.io.Serializable;
 
+import core.algorithm.SingleObjectiveEA;
 import core.points.Solution;
+import operators.impl.crossover.SBX;
+import operators.impl.mutation.PolynomialMutation;
+import operators.impl.selection.BinaryTournament;
+import problems.dtlz.DTLZ4;
+import solutionRankers.ChebyshevRanker;
 import utils.NSGAIIIRandom;
+import utils.PythonVisualizer;
 
 public abstract class Problem implements Serializable {
 	/**
@@ -59,6 +66,44 @@ public void evaluate(Population pop) {
 			evaluate(s);
 		}
 	}
+
+	public double[] findIdealPoint(){
+		double lambda[] = new double[numObjectives];
+		double idealPoint[] = new double[numObjectives];
+
+		for(int i=0; i<idealPoint.length; i++){
+			idealPoint[i] = Double.MAX_VALUE;
+		}
+
+		for(int optimizedDim=0; optimizedDim < numObjectives; optimizedDim++){
+			for(int i=0; i<lambda.length; i++){
+				lambda[i] = 0;
+			}
+			lambda[optimizedDim] = 1;
+			ChebyshevRanker cr = new ChebyshevRanker(lambda);
+			int numGenerations = 100;
+
+			SingleObjectiveEA so = new SingleObjectiveEA(	
+				this,  
+				new BinaryTournament(cr),
+				new SBX(1.0, 30.0, lowerBound, upperBound),
+				new PolynomialMutation(1.0 / numVariables, 20.0, lowerBound, upperBound),
+				cr,
+				90 //population size
+			);
+			for(int i=0; i < numGenerations; i++){
+				so.nextGeneration();
+			}
+			//Workaround for inner class error
+		    final int dummyOptimizedDim = optimizedDim;
+			idealPoint[optimizedDim] = so.getPopulation().getSolutions().stream().mapToDouble(s -> s.getObjective(dummyOptimizedDim)).min().getAsDouble();
+
+			Population finalPop = so.getPopulation();
+			PythonVisualizer pv = new PythonVisualizer();
+			pv.visualise(getReferenceFront(), finalPop);
+		}
+		return idealPoint;
+	}
 
 	// TODO
 	public abstract void evaluateConstraints(Solution solution);

src/core/algorithm/SingleObjectiveEA.java

@@ -0,0 +1,32 @@
+package core.algorithm;
+
+import java.util.Comparator;
+
+import core.Population;
+import core.Problem;
+import core.points.Solution;
+import operators.CrossoverOperator;
+import operators.MutationOperator;
+import operators.SelectionOperator;
+import solutionRankers.ChebyshevRanker;
+
+public class SingleObjectiveEA extends EA{
+
+	protected Comparator <Solution> comp;
+	protected int popSize;
+	public SingleObjectiveEA(Problem problem, SelectionOperator selectionOperator, CrossoverOperator crossoverOperator,
+			MutationOperator mutationOperator, ChebyshevRanker cr, int popSize) {
+		super(problem, selectionOperator, crossoverOperator, mutationOperator);
+		this.popSize = popSize;
+		population = problem.createPopulation(popSize);
+		comp = cr;
+	}
+
+	@Override
+	protected Population selectNewPopulation(Population pop) {
+		pop.getSolutions().sort(comp);
+		Population res = new Population(pop.getSolutions().subList(0, popSize));
+		return res;
+	}
+
+}