This pull request adds the Market Game Optimization Algorithm (MGOA) to Mealpy.

Algorithm

• Name: Market Game Optimization Algorithm (MGOA)
• Inspiration: Symmetric competitive behavior between merchants and consumers
• Category: Human-based optimizer

Reference

• Authors: Shenglin Liu, Yuqing Xiang, Xuping Guo, Feng Zhao, Aiping Zhao, Wangyu Wu
• Journal: Symmetry (2025)
• DOI: https://doi.org/10.3390/sym17122118

Implementation details

• Implemented as OriginalMGOA under mealpy/human_based
• Follows Mealpy optimizer interface and conventions
• Bounds handling is enforced after each update
• Default parameters follow the original paper (epoch=5000, pop_size=50)

Example

• A runnable example is provided in examples/run_mgoa.py
• Verified locally on a 30D Sphere problem with decreasing fitness across epochs

Note

Mealpy already includes an algorithm named MGO, which is a different method.
This implementation uses the name MGOA to avoid ambiguity.