This repository contains the implementations and resources for three optimization algorithms: Improved Atom Search Optimization (IASO), Social Mimetic Optimizer (SMO), and Barnacles Mating Optimizer (BMO). These algorithms are designed to solve various optimization problems efficiently.
- CODE_PYTHON/: Contains the Python implementations of the algorithms.
- PRESENTATIONS/: Includes presentation materials for each algorithm.
- RESOURCES/: Contains reference articles and additional resources used for the implementations.
IASO is an enhanced version of the Atom Search Optimization (ASO) algorithm. It includes improved mechanisms for global and local search to ensure better performance in finding optimal solutions.
- Enhanced search capabilities
- Balanced exploration and exploitation
- Suitable for various optimization problems
SMO is a memetic algorithm that combines social and cultural evolution concepts with traditional optimization techniques. It leverages social learning and individual learning to improve the search process.
- Integrates social and individual learning
- Adaptable to different types of optimization problems
- Utilizes a hybrid approach for better optimization
BMO is inspired by the mating behavior of barnacles. It utilizes their unique reproductive strategies to explore and exploit the search space effectively.
- Inspired by natural mating behaviors
- Effective in finding optimal solutions
- Competitive with other evolutionary algorithms
Contributions are welcome! Please submit a pull request or open an issue for any changes or additions you would like to make.