diff --git a/Metaheuristics/GTO/GTO.java b/Metaheuristics/GTO/GTO.java
new file mode 100644
index 0000000..43c33d8
--- /dev/null
+++ b/Metaheuristics/GTO/GTO.java
@@ -0,0 +1,176 @@
+package Metaheuristics.GTO;
+
+import Metaheuristics.ObjectiveFunction;
+
+import java.util.Random;
+
+public class GTO{
+
+    private final int popSize;
+    private final int maxIter;
+    private final double[] lb;
+    private final double[] ub;
+    private final int varSize;
+
+    private final ObjectiveFunction fitFunc;
+    double[] silverback;
+    double silverbackScore = Double.POSITIVE_INFINITY;
+
+    public GTO(ObjectiveFunction objectiveFunction, int popSize, int varSize, int maxIter, double[] lb, double[] ub) {
+        this.popSize = popSize;
+        this.maxIter = maxIter;
+        this.lb = lb;
+        this.ub = ub;
+        this.varSize = varSize;
+        this.fitFunc = objectiveFunction;
+    }
+
+    public double[] run() {
+        silverback = new double[varSize];
+        // Initialize the first random population of Gorilla
+        double[][] x = initialization(popSize, varSize, ub, lb);
+
+
+        double[] popFit = new double[popSize];
+
+        // Main loop
+        for (int it = 1; it <= maxIter; it++) {
+            double a = (Math.cos(2 * Math.random()) + 1) * (1 - (double) it / maxIter);
+            double c = a * (2 * Math.random() - 1);
+
+            // Exploration phase
+            for (int i = 0; i < popSize; i++) {
+                if (Math.random() < 0.03) {
+                    for (int j = 0; j < varSize; j++) {
+                        x[i][j] = (ub[j] - lb[j]) * Math.random() + lb[j];
+                    }
+                } else {
+                    if (Math.random() >= 0.5) {
+                        double[] z = new double[varSize];
+                        for (int j = 0; j < varSize; j++) {
+                            z[j] = Math.random() * (2 * a) - a;
+                        }
+
+                        double[] h = new double[varSize];
+                        for (int j = 0; j < varSize; j++) {
+                            h[j] = z[j] * x[i][j];
+                        }
+
+                        int randomIndex = new Random().nextInt(popSize);
+                        double[] randX = x[randomIndex];
+
+                        double[] gx = new double[varSize];
+                        for (int j = 0; j < varSize; j++) {
+                            gx[j] = (Math.random() - a) * randX[j] + c * h[j];
+                        }
+
+                        x[i] = gx;
+                    } else {
+                        double[] gx = new double[varSize];
+                        for (int j = 0; j < varSize; j++) {
+                            gx[j] = x[i][j] - c * (c * (x[i][j] - x[new Random().nextInt(popSize)][j]) + Math.random() * (x[i][j] - x[new Random().nextInt(popSize)][j]));
+                        }
+                        x[i] = gx;
+                    }
+                }
+            }
+
+            boundaryCheck(x, lb, ub);
+
+            // Group formation operation
+            for (int i = 0; i < popSize; i++) {
+                double newFit = fitFunc.evaluate(x[i]);
+                popFit[i] = newFit;
+
+                if (newFit < silverbackScore) {
+                    silverbackScore = newFit;
+                    silverback = x[i].clone();
+                }
+            }
+
+            // Exploitation phase
+            for (int i = 0; i < popSize; i++) {
+                if (a >= 0.8) {
+                    double g = Math.pow(2, c);
+                    double[] delta = new double[varSize];
+                    for (int j = 0; j < varSize; j++) {
+                        delta[j] = Math.pow(Math.abs(mean(x, j)), g) * Math.pow(1.0 / varSize, 1.0 / g);
+                    }
+
+                    double[] gx = new double[varSize];
+                    for (int j = 0; j < varSize; j++) {
+                        gx[j] = c * delta[j] * (x[i][j] - silverback[j]) + x[i][j];
+                    }
+
+                    x[i] = gx;
+                } else {
+                    double[] gx = new double[varSize];
+                    for (int j = 0; j < varSize; j++) {
+                        if (Math.random() >= 0.5) {
+                            gx[j] = silverback[j] - (silverback[j] * (2 * Math.random() - 1) - x[i][j] * (2 * Math.random() - 1)) * (3 * h() / 10);
+                        } else {
+                            gx[j] = silverback[j] - (silverback[j] * (2 * Math.random() - 1) - x[i][j] * (2 * Math.random() - 1)) * (1 / h());
+                        }
+                    }
+                    x[i] = gx;
+                }
+            }
+
+            boundaryCheck(x, lb, ub);
+
+            // Group formation operation
+            for (int i = 0; i < popSize; i++) {
+                double newFit = fitFunc.evaluate(x[i]);
+                popFit[i] = newFit;
+
+                if (newFit < silverbackScore) {
+                    silverbackScore = newFit;
+                    silverback = x[i].clone();
+                }
+            }
+
+            System.out.printf("In Iteration %d, best estimation of the global optimum is %4.4f\n", it, silverbackScore);
+        }
+
+        return silverback;
+    }
+
+    private static double[][] initialization(int N, int dim, double[] ub, double[] lb) {
+        double[][] X = new double[N][dim];
+        Random random = new Random();
+        for (int i = 0; i < N; i++) {
+            for (int j = 0; j < dim; j++) {
+                X[i][j] = random.nextDouble() * (ub[j] - lb[j]) + lb[j];
+            }
+        }
+        return X;
+    }
+
+    private static void boundaryCheck(double[][] X, double[] lb, double[] ub) {
+        for (int i = 0; i < X.length; i++) {
+            for (int j = 0; j < X[i].length; j++) {
+                if (X[i][j] < lb[j]) {
+                    X[i][j] = lb[j];
+                } else if (X[i][j] > ub[j]) {
+                    X[i][j] = ub[j];
+                }
+            }
+        }
+    }
+
+    private static double mean(double[][] X, int j) {
+        double sum = 0;
+        for (double[] x : X) {
+            sum += x[j];
+        }
+        return sum / X.length;
+    }
+
+    private static double h() {
+        if (Math.random() >= 0.5) {
+            return 1.0;
+        } else {
+            return 1.0 / 10.0;
+        }
+    }
+}