Add DBO algorithm implementation

[hidroksit]

📑 Description

I have implemented the Dung Beetle Optimizer (DBO) algorithm for the swarm_based module, following the methodology described in the original DBO paper by Xue and Shen (2023). This implementation follows the original mathematical formulas, incorporating the ball-rolling, dancing, foraging, stealing, and reproduction behaviors.

To ensure computational efficiency and integration with the latest mealpy (v3.0+) standards, the algorithm is built using fully vectorized NumPy operations and inherits directly from the Optimizer class with the new Agent object structure.

• Implementation of the algorithm in Python/mealpy standards [x] Completed
• Adding DBO to the mealpy/swarm_based/__init__.py file [x] Completed
• Verified compatibility with Mealpy v3.0+ Agent structure [x] Completed

✅ Checks

• My pull request adheres to the code style of this project
• My code requires changes to the documentation
• I have updated the documentation as required
• All the tests have passed

ℹ Additional Information

Comparison of CEC-2017 tests between original paper results & python implementation results:

The algorithm was tested on CEC-2017 benchmark functions. The "Article result" column reflects the values reported in the original paper (Table 4) with Dim=10.

Configuration: Pop = 30, Epoch = 50 (Python) vs 10000 (Paper)

Function	Type	Python ver. result (My Run)	Article result (Mean)*
F1	Unimodal	4.15E+03	3.15E+03
F5	Simple Multimodal	5.94E+02	5.38E+02
F9	Simple Multimodal	8.97E+02	9.21E+02

*Article results source: Xue & Shen (2023), Table 4

Reference:

Paper: Xue, J., & Shen, B. (2023). Dung beetle optimizer: a new meta-heuristic algorithm for global optimization. The Journal of Supercomputing, 79(7), 7305-7336.
DOI: 10.1007/s11227-022-04959-6