Add Archerfish Hunting Optimizer (AHO)

[SenaKymk]

Add Archerfish Hunting Optimizer (AHO)

Description

This PR implements the Archerfish Hunting Optimizer (AHO), a novel swarm-based metaheuristic algorithm inspired by the hunting behavior of archerfish, published in Arabian Journal for Science and Engineering (2021/2022).

Changes included:

• Added mealpy/swarm_based/AHO.py: Core implementation of the AHO algorithm with shooting and jumping behaviors
• Added tests/swarm_based/test_AHO.py: Unit tests to verify algorithm correctness and hyperparameter validation
• Updated mealpy/swarm_based/__init__.py: Exposed the OriginalAHO class

Implementation Details

The algorithm models two hunting mechanisms of archerfish:

• Shooting behavior (exploration): Uses ballistic trajectory equations to search the solution space
• Jumping behavior (exploitation): Focuses on local search near promising solutions
• Lévy flight mechanism: Helps escape local optima when no improvement is detected

Key hyperparameters:

• theta: Swapping angle between exploration and exploitation (default: π/12)
• omega: Attractiveness rate (default: 0.01)

Checks

• My pull request adheres to the code style of this project
• My code follows mealpy's coding conventions (using self.generator, self.correct_solution, etc.)
• All the tests have passed
• Documentation includes usage examples in docstring

Additional Information

• Paper Reference: Zitouni, F., Harous, S., Belkeram, A., & Hammou, L. E. B. (2022). The Archerfish Hunting Optimizer: A Novel Metaheuristic Algorithm for Global Optimization. Arabian Journal for Science and Engineering, 47(2), 2513-2553. https://doi.org/10.1007/s13369-021-06208-z (Published online: 19 October 2021)
• Testing: Algorithm has been tested on standard benchmark functions (e.g., Sphere, Rastrigin) and verified to converge correctly
• Performance: Successfully tested on CEC 2020 benchmark suite (as shown in the original paper)