diff --git a/mealpy/swarm_based/DBO.PY b/mealpy/swarm_based/DBO.PY
new file mode 100644
index 00000000..54ab1094
--- /dev/null
+++ b/mealpy/swarm_based/DBO.PY
@@ -0,0 +1,103 @@
+import numpy as np
+from mealpy.optimizer import Optimizer
+
+class DBO(Optimizer):
+    """
+    Dung Beetle Optimizer (DBO)
+    
+    Links:
+        1. https://doi.org/10.1007/s11227-022-04959-6
+        
+    Notes:
+        The algorithm mimics the ball-rolling, dancing, foraging, stealing, and reproduction behaviors of dung beetles.
+        Implementation based on the original paper logic.
+    """
+
+    def __init__(self, epoch=10000, pop_size=100, **kwargs):
+        """
+        Args:
+            epoch (int): Maximum number of iterations, default = 10000
+            pop_size (int): Number of population size, default = 100
+        """
+        super().__init__(**kwargs)
+        self.epoch = self.validator.check_int("epoch", epoch, [1, 100000])
+        self.pop_size = self.validator.check_int("pop_size", pop_size, [10, 10000])
+        self.set_parameters(["epoch", "pop_size"])
+        self.sort_flag = True
+
+    def evolve(self, epoch):
+        """
+        The main operations (evolve) of the algorithm.
+        """
+        # Define population size for each group based on paper (approximate percentages)
+        n_roll = int(self.pop_size * 0.2)
+        n_brood = int(self.pop_size * 0.2)
+        n_small = int(self.pop_size * 0.25)
+        # The rest are thieves
+        
+        # Current Global Best and Worst
+        pos_best = self.g_best[self.ID_POS]           # X^b
+        pos_worst = self.pop[-1][self.ID_POS]         # X^w
+        pos_local_best = self.pop[0][self.ID_POS]     # X^*
+
+        # R factor (decreases linearly)
+        R = 1 - (epoch / self.epoch)
+
+        pop_new = []
+        for idx in range(0, self.pop_size):
+            pos_now = self.pop[idx][self.ID_POS]
+            pos_new = pos_now.copy()
+
+            # --- 1. Ball-Rolling Dung Beetles ---
+            if idx < n_roll:
+                # Obstacle simulation (prob = 0.9)
+                if np.random.rand() < 0.9:
+                    # Rolling with celestial cues (Eq. 1)
+                    alpha = 1 if np.random.rand() > 0.5 else -1
+                    k = 0.1
+                    b = 0.3
+                    delta_x = np.abs(pos_now - pos_worst)
+                    pos_new = pos_now + alpha * k * pos_now + b * delta_x
+                else:
+                    # Dancing (Eq. 2)
+                    theta = np.random.uniform(0, np.pi)
+                    if theta != 0 and theta != np.pi / 2 and theta != np.pi:
+                        pos_new = pos_now + np.tan(theta) * np.abs(pos_now)
+
+            # --- 2. Brood Balls (Reproduction) ---
+            elif idx < n_roll + n_brood:
+                # Spawning area boundaries (Eq. 3)
+                lb_star = np.maximum(pos_local_best * (1 - R), self.problem.lb)
+                ub_star = np.minimum(pos_local_best * (1 + R), self.problem.ub)
+                
+                # Position update (Eq. 4)
+                b1 = np.random.rand(self.problem.n_dims)
+                b2 = np.random.rand(self.problem.n_dims)
+                pos_new = pos_local_best + b1 * (pos_now - lb_star) + b2 * (pos_now - ub_star)
+
+            # --- 3. Small Dung Beetles (Foraging) ---
+            elif idx < n_roll + n_brood + n_small:
+                # Foraging area (Eq. 5)
+                lb_b = np.maximum(pos_best * (1 - R), self.problem.lb)
+                ub_b = np.minimum(pos_best * (1 + R), self.problem.ub)
+                
+                # Position update (Eq. 6)
+                C1 = np.random.normal(0, 1)
+                C2 = np.random.rand(self.problem.n_dims)
+                pos_new = pos_now + C1 * (pos_now - lb_b) + C2 * (pos_now - ub_b)
+
+            # --- 4. Thieves (Stealing) ---
+            else:
+                # Stealing behavior (Eq. 7)
+                S = 0.5
+                g = np.random.normal(0, 1, self.problem.n_dims)
+                pos_new = pos_best + S * g * (np.abs(pos_now - pos_local_best) + np.abs(pos_now - pos_best))
+
+            # Boundary check
+            pos_new = self.amend_position(pos_new, self.problem.lb, self.problem.ub)
+            pop_new.append([pos_new, None])
+            
+            if self.mode not in self.AVAILABLE_MODES:
+                pop_new[-1][self.ID_TAR] = self.get_target_wrapper(pos_new)
+
+        self.pop = self.update_target_wrapper_population(pop_new)
\ No newline at end of file
diff --git a/mealpy/swarm_based/__init__.py b/mealpy/swarm_based/__init__.py
index 90902d0d..e51298b7 100644
--- a/mealpy/swarm_based/__init__.py
+++ b/mealpy/swarm_based/__init__.py
@@ -3,3 +3,4 @@
 #       Email: nguyenthieu2102@gmail.com            %
 #       Github: https://github.com/thieu1995        %
 # --------------------------------------------------%
+from . import DBO
\ No newline at end of file