Description
Description
I would like to request the addition of the Market Game Optimization Algorithm (MGOA), a recently proposed metaheuristic inspired by the symmetric competitive behavior between merchants and consumers in a market game environment.
MGOA models the dynamic interactions between sellers and buyers as a population-based optimization process, where individuals update their positions based on competitive pricing strategies, mutual influence, and global market equilibrium. The algorithm is designed to balance exploration and exploitation through probabilistic phase selection and adaptive interaction mechanisms.
Unlike classical economic-inspired optimizers, MGOA explicitly incorporates symmetric competition dynamics, allowing agents to simultaneously influence and respond to each other, which improves population diversity and convergence stability.
Reference
Benchmark Validation
According to the paper, MGOA has been extensively evaluated on:
- CEC2017 benchmark functions
- CEC2022 benchmark functions
- Multiple problem dimensions (e.g., 30D, 50D, 100D)
- Several real-world engineering design optimization problems
The results demonstrate that MGOA outperforms or competes favorably with several state-of-the-art metaheuristic algorithms in terms of solution quality, convergence accuracy, and robustness.
Since MEALPY already supports CEC benchmark suites via opfunu, integrating MGOA would be straightforward and consistent with existing benchmarking workflows.
Additional Information
- MGOA is not currently implemented in MEALPY
- There is no existing open issue requesting MGOA
- Although MEALPY includes an algorithm named
MGO, it represents a different method; therefore, the name MGOA is suggested to avoid ambiguity
Possible Implementation Notes
- Suggested module name:
MGOA
- Suggested class name:
OriginalMGOA
- The algorithm could be placed under an appropriate subpackage (e.g.,
human_based or economic_based) following MEALPY’s design conventions
- Default parameters and update rules can be implemented according to the original paper
If the maintainers agree, I would be happy to contribute an implementation that follows MEALPY’s coding style and provide example benchmark scripts.
Additional Information
No response
Description
Description
I would like to request the addition of the Market Game Optimization Algorithm (MGOA), a recently proposed metaheuristic inspired by the symmetric competitive behavior between merchants and consumers in a market game environment.
MGOA models the dynamic interactions between sellers and buyers as a population-based optimization process, where individuals update their positions based on competitive pricing strategies, mutual influence, and global market equilibrium. The algorithm is designed to balance exploration and exploitation through probabilistic phase selection and adaptive interaction mechanisms.
Unlike classical economic-inspired optimizers, MGOA explicitly incorporates symmetric competition dynamics, allowing agents to simultaneously influence and respond to each other, which improves population diversity and convergence stability.
Reference
Benchmark Validation
According to the paper, MGOA has been extensively evaluated on:
The results demonstrate that MGOA outperforms or competes favorably with several state-of-the-art metaheuristic algorithms in terms of solution quality, convergence accuracy, and robustness.
Since MEALPY already supports CEC benchmark suites via opfunu, integrating MGOA would be straightforward and consistent with existing benchmarking workflows.
Additional Information
MGO, it represents a different method; therefore, the name MGOA is suggested to avoid ambiguityPossible Implementation Notes
MGOAOriginalMGOAhuman_basedoreconomic_based) following MEALPY’s design conventionsIf the maintainers agree, I would be happy to contribute an implementation that follows MEALPY’s coding style and provide example benchmark scripts.
Additional Information
No response