diff --git a/README.md b/README.md
index 47f94383..f7ac7c68 100644
--- a/README.md
+++ b/README.md
@@ -1858,6 +1858,15 @@ along with their syntax and common problem applications. This will guide you in
| 5 |
easy |
+
+ | Bio |
+ Secretary Bird Optimization Algorithm |
+ SBOA |
+ OriginalSBOA |
+ 2024 |
+ 2 |
+ easy |
+
| Bio |
Earthworm Optimisation Algorithm |
@@ -3243,6 +3252,8 @@ All visualization examples: [Link](https://mealpy.readthedocs.io/en/latest/pages
* **SBO - Satin Bowerbird Optimizer**
* **OriginalSBO**: Moosavi, S. H. S., & Bardsiri, V. K. (2017). Satin bowerbird optimizer: a new optimization algorithm to optimize ANFIS for software development effort estimation. Engineering Applications of Artificial Intelligence, 60, 1-15.
* **BaseSBO**: The developed version
+* **SBOA - Secretary Bird Optimization Algorithm**
+ * **OriginalSBOA**: Fu, Y., Liu, D., Chen, J. et al. (2024). Secretary bird optimization algorithm: a new metaheuristic for solving global optimization problems. Artificial Intelligence Review, 57, 123. https://doi.org/10.1007/s10462-024-10729-y
* **SHO - Spotted Hyena Optimizer**
* **OriginalSHO**: Dhiman, G., & Kumar, V. (2017). Spotted hyena optimizer: a novel bio-inspired based metaheuristic technique for engineering applications. Advances in Engineering Software, 114, 48-70.
@@ -3405,4 +3416,4 @@ All visualization examples: [Link](https://mealpy.readthedocs.io/en/latest/pages
---
-Developed by: [Thieu](mailto:nguyenthieu2102@gmail.com?Subject=MEALPY_QUESTIONS) @ 2022
\ No newline at end of file
+Developed by: [Thieu](mailto:nguyenthieu2102@gmail.com?Subject=MEALPY_QUESTIONS) @ 2022
diff --git a/mealpy/bio_based/SBOA.py b/mealpy/bio_based/SBOA.py
new file mode 100644
index 00000000..94ff1e77
--- /dev/null
+++ b/mealpy/bio_based/SBOA.py
@@ -0,0 +1,155 @@
+#!/usr/bin/env python
+# Created by "Thieu" at 13:00, 05/01/2026 ----------%
+# Email: nguyenthieu2102@gmail.com %
+# Github: https://github.com/thieu1995 %
+# --------------------------------------------------%
+
+import numpy as np
+from mealpy.optimizer import Optimizer
+
+
+class OriginalSBOA(Optimizer):
+ """
+ The original version of: Secretary Bird Optimization Algorithm (SBOA)
+
+ Links:
+ 1. https://doi.org/10.1007/s10462-024-10729-y
+ 2. https://www.mathworks.com/matlabcentral/fileexchange/164456-secretary-bird-optimization-algorithm-sboa
+
+ Examples
+ ~~~~~~~~
+ >>> import numpy as np
+ >>> from mealpy import FloatVar, SBOA
+ >>>
+ >>> def objective_function(solution):
+ >>> return np.sum(solution**2)
+ >>>
+ >>> problem_dict = {
+ >>> "bounds": FloatVar(lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
+ >>> "obj_func": objective_function,
+ >>> "minmax": "min",
+ >>> }
+ >>>
+ >>> model = SBOA.OriginalSBOA(epoch=1000, pop_size=50)
+ >>> g_best = model.solve(problem_dict)
+ >>> print(f"Solution: {g_best.solution}, Fitness: {g_best.target.fitness}")
+ >>> print(f"Solution: {model.g_best.solution}, Fitness: {model.g_best.target.fitness}")
+
+ References
+ ~~~~~~~~~~
+ [1] Fu, Y., Liu, D., Chen, J. et al. Secretary bird optimization algorithm: a new metaheuristic
+ for solving global optimization problems. Artif Intell Rev 57, 123 (2024).
+ https://doi.org/10.1007/s10462-024-10729-y
+ """
+
+ def __init__(self, epoch: int = 10000, pop_size: int = 100, **kwargs: object) -> None:
+ """
+ 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, [5, 10000])
+ self.set_parameters(["epoch", "pop_size"])
+ self.sort_flag = False
+
+ def get_levy_flight_step(self, beta=1.5, multiplier=1.0, size=None):
+ """
+ Get Levy flight step
+
+ Args:
+ beta (float): Levy exponent, default = 1.5
+ multiplier (float): Multiplier factor, default = 1.0
+ size (int): Size of levy step, default = None
+
+ Returns:
+ numpy.ndarray: Levy flight step
+ """
+ if size is None:
+ size = self.problem.n_dims
+ sigma = (np.math.gamma(1 + beta) * np.sin(np.pi * beta / 2) /
+ (np.math.gamma((1 + beta) / 2) * beta * 2 ** ((beta - 1) / 2))) ** (1 / beta)
+ u = self.generator.normal(0, sigma, size)
+ v = self.generator.normal(0, 1, size)
+ step = multiplier * u / (np.abs(v) ** (1 / beta))
+ return step
+
+ def evolve(self, epoch):
+ """
+ The main operations (equations) of algorithm. Inherit from Optimizer class
+
+ Args:
+ epoch (int): The current iteration
+ """
+ t = epoch
+ T = self.epoch
+
+ # Calculate Convergence Factor (Eq. 9)
+ CF = (1 - t/T) ** (2 * t/T)
+
+ pop_new = []
+
+ # Hunting Strategies (Exploration Phase)
+ for idx in range(0, self.pop_size):
+ if t < T / 3:
+ # Stage 1: Secretary bird search prey (Eq. 4-5)
+ idxs = self.generator.choice(list(range(0, self.pop_size)), 2, replace=True)
+ x_rand1 = self.pop[idxs[0]].solution
+ x_rand2 = self.pop[idxs[1]].solution
+ R1 = self.generator.random(self.problem.n_dims)
+ pos_new = self.pop[idx].solution + (x_rand1 - x_rand2) * R1
+
+ elif t < 2 * T / 3:
+ # Stage 2: Secretary bird approaching prey (Eq. 7-8)
+ RB = self.generator.normal(0, 1, self.problem.n_dims)
+ term = np.exp((t/T)**4)
+ pos_new = self.g_best.solution + term * (RB - 0.5) * (self.g_best.solution - self.pop[idx].solution)
+
+ else:
+ # Stage 3: Secretary bird attacks prey (Eq. 9-10)
+ levy_step = self.get_levy_flight_step(beta=1.5, multiplier=1.0)
+ RL = 0.5 * levy_step
+ pos_new = self.g_best.solution + CF * self.pop[idx].solution * RL
+
+ pos_new = self.correct_solution(pos_new)
+ agent = self.generate_empty_agent(pos_new)
+ pop_new.append(agent)
+ if self.mode not in self.AVAILABLE_MODES:
+ agent.target = self.get_target(pos_new)
+ self.pop[idx] = self.get_better_agent(agent, self.pop[idx], self.problem.minmax)
+
+ # Update population after exploration
+ if self.mode in self.AVAILABLE_MODES:
+ pop_new = self.update_target_for_population(pop_new)
+ self.pop = self.greedy_selection_population(self.pop, pop_new, self.problem.minmax)
+
+ # Escaping Strategies (Exploitation Phase)
+ r = self.generator.random()
+ k = self.generator.integers(0, self.pop_size)
+ x_random_global = self.pop[k].solution
+
+ pop_new_escape = []
+ for idx in range(0, self.pop_size):
+ if r < 0.5:
+ # C1: Secretary birds use their environment to hide (Eq. 14)
+ RB = self.generator.random(self.problem.n_dims)
+ factor = (1 - t/T) ** 2
+ pos_new = self.g_best.solution + factor * (2 * RB - 1) * self.pop[idx].solution
+ else:
+ # C2: Secretary birds fly or run away (Eq. 14, 16)
+ K = int(round(1 + self.generator.random()))
+ R2 = self.generator.random(self.problem.n_dims)
+ pos_new = self.pop[idx].solution + R2 * (x_random_global - K * self.pop[idx].solution)
+
+ pos_new = self.correct_solution(pos_new)
+ agent = self.generate_empty_agent(pos_new)
+ pop_new_escape.append(agent)
+ if self.mode not in self.AVAILABLE_MODES:
+ agent.target = self.get_target(pos_new)
+ self.pop[idx] = self.get_better_agent(agent, self.pop[idx], self.problem.minmax)
+
+ # Update population after exploitation
+ if self.mode in self.AVAILABLE_MODES:
+ pop_new_escape = self.update_target_for_population(pop_new_escape)
+ self.pop = self.greedy_selection_population(self.pop, pop_new_escape, self.problem.minmax)
diff --git a/tests/bio_based/test_SBOA.py b/tests/bio_based/test_SBOA.py
new file mode 100644
index 00000000..adfeaf3d
--- /dev/null
+++ b/tests/bio_based/test_SBOA.py
@@ -0,0 +1,66 @@
+#!/usr/bin/env python
+# Created by "Thieu" at 13:00, 05/01/2026 ----------%
+# Email: nguyenthieu2102@gmail.com %
+# Github: https://github.com/thieu1995 %
+# --------------------------------------------------%
+
+from mealpy import FloatVar, SBOA, Optimizer
+import numpy as np
+import pytest
+
+
+@pytest.fixture(scope="module") # scope: Call only 1 time at the beginning
+def problem():
+ def objective_function(solution):
+ return np.sum(solution ** 2)
+
+ problem = {
+ "obj_func": objective_function,
+ "bounds": FloatVar(lb=[-10, -10, -10, -10, -10], ub=[10, 10, 10, 10, 10]),
+ "minmax": "min",
+ }
+ return problem
+
+
+def test_OriginalSBOA_results(problem):
+ epoch = 10
+ pop_size = 50
+ model = SBOA.OriginalSBOA(epoch, pop_size)
+ g_best = model.solve(problem)
+ assert isinstance(model, Optimizer)
+ assert isinstance(g_best.solution, np.ndarray)
+ assert len(g_best.solution) == len(model.problem.lb)
+
+
+@pytest.mark.parametrize("problem, epoch, system_code",
+ [
+ (problem, None, 0),
+ (problem, "hello", 0),
+ (problem, -10, 0),
+ (problem, [10], 0),
+ (problem, (0, 9), 0),
+ (problem, 0, 0),
+ (problem, float("inf"), 0),
+ ])
+def test_epoch_SBOA(problem, epoch, system_code):
+ pop_size = 50
+ with pytest.raises(ValueError) as e:
+ SBOA.OriginalSBOA(epoch, pop_size)
+ assert e.type == ValueError
+
+
+@pytest.mark.parametrize("problem, pop_size, system_code",
+ [
+ (problem, None, 0),
+ (problem, "hello", 0),
+ (problem, -10, 0),
+ (problem, [10], 0),
+ (problem, (0, 9), 0),
+ (problem, 0, 0),
+ (problem, float("inf"), 0),
+ ])
+def test_pop_size_SBOA(problem, pop_size, system_code):
+ epoch = 10
+ with pytest.raises(ValueError) as e:
+ SBOA.OriginalSBOA(epoch, pop_size)
+ assert e.type == ValueError