Description
Description
I would like to request the addition of the Kepler Optimization Algorithm (KOA) to the Mealpy library.
KOA is a population-based metaheuristic optimization algorithm inspired by Kepler’s laws of planetary motion, where candidate solutions evolve through gravitational attraction mechanisms that balance exploration and exploitation. The algorithm models the movement of agents around a central mass, allowing efficient global search and convergence toward optimal solutions.
Unlike many recently proposed metaheuristics, KOA is clearly defined, benchmark-driven, and does not rely on ambiguous or undocumented parameters, which makes it suitable for practical implementation and fair comparison.
Key characteristics of KOA:
Population-based optimization framework
Update mechanism inspired by Kepler’s laws and gravitational interaction
Explicit control parameters (population size, number of iterations, etc.)
Designed for continuous optimization problems
Benchmark evaluation based on CEC test functions
Why KOA fits well in Mealpy
The algorithm structure is compatible with existing Mealpy optimizers
Benchmark settings (CEC functions, dimensions, iterations) are clearly stated in the paper
No external or proprietary components are required
KOA is not currently available in the Mealpy repository
Published after 2015, with a well-documented experimental setup
Given that Mealpy focuses on reproducible, well-defined metaheuristic algorithms, KOA would be a meaningful addition to the library.
Reference
Author(s). Kepler Optimization Algorithm. Applied Soft Computing, 2023.
DOI / Paper link: [buraya makale linkini koy]
If this feature aligns with the project roadmap, I would be happy to help with testing, validation, or implementation following Mealpy’s structure and conventions.
Additional Information
No response
Description
Description
I would like to request the addition of the Kepler Optimization Algorithm (KOA) to the Mealpy library.
KOA is a population-based metaheuristic optimization algorithm inspired by Kepler’s laws of planetary motion, where candidate solutions evolve through gravitational attraction mechanisms that balance exploration and exploitation. The algorithm models the movement of agents around a central mass, allowing efficient global search and convergence toward optimal solutions.
Unlike many recently proposed metaheuristics, KOA is clearly defined, benchmark-driven, and does not rely on ambiguous or undocumented parameters, which makes it suitable for practical implementation and fair comparison.
Key characteristics of KOA:
Population-based optimization framework
Update mechanism inspired by Kepler’s laws and gravitational interaction
Explicit control parameters (population size, number of iterations, etc.)
Designed for continuous optimization problems
Benchmark evaluation based on CEC test functions
Why KOA fits well in Mealpy
The algorithm structure is compatible with existing Mealpy optimizers
Benchmark settings (CEC functions, dimensions, iterations) are clearly stated in the paper
No external or proprietary components are required
KOA is not currently available in the Mealpy repository
Published after 2015, with a well-documented experimental setup
Given that Mealpy focuses on reproducible, well-defined metaheuristic algorithms, KOA would be a meaningful addition to the library.
Reference
Author(s). Kepler Optimization Algorithm. Applied Soft Computing, 2023.
DOI / Paper link: [buraya makale linkini koy]
If this feature aligns with the project roadmap, I would be happy to help with testing, validation, or implementation following Mealpy’s structure and conventions.
Additional Information
No response