Citation Request

If you utilize MEALPY in your academic or professional research, we kindly request that you cite our foundational works to support the project:

@article{van2023mealpy,
    title={MEALPY: An open-source library for latest meta-heuristic algorithms in Python},
    author={Van Thieu, Nguyen and Mirjalili, Seyedali},
    journal={Journal of Systems Architecture},
    year={2023},
    publisher={Elsevier},
    doi={10.1016/j.sysarc.2023.102871}
}

@article{van2023groundwater,
    title={Groundwater level modeling using Augmented Artificial Ecosystem Optimization},
    author={Van Thieu, Nguyen and Barma, Surajit Deb and Van Lam, To and Kisi, Ozgur and Mahesha, Amai},
    journal={Journal of Hydrology},
    volume={617},
    pages={129034},
    year={2023},
    publisher={Elsevier},
    doi={10.1016/j.jhydrol.2022.129034}
}

Note

Contact for Research & Collaboration

If you have an open-ended question, a research inquiry, or are interested in collaboration, feel free to contact the author directly at: nguyenthieu2102@gmail.com

Classification Table

Warning

Legacy Classification

The categorical breakdown below is conceptual. For the most up-to-date, exhaustive list of supported optimizers, please refer directly to the Official Optimizer Classification Table on our GitHub repository.

Categories of Meta-heuristic Algorithms

Based on this comprehensive review article, algorithms are generally grouped into:

  • Evolutionary-based: Inspired by Darwin’s laws of natural selection and evolutionary computing.

  • Swarm-based: Inspired by the movement, interaction, and organization of birds, social insects, and other animals.

  • Physics-based: Inspired by physical laws such as Newton’s law of universal gravitation, black holes, and the multiverse.

  • Human-based: Inspired by human interactions, such as queuing search, teaching-learning, and cultural algorithms.

  • Biology-based: Inspired by biological creatures or microorganisms (e.g., genetic algorithms and artificial immune systems).

  • System-based: Inspired by ecosystems, immune systems, and network systems.

  • Math-based: Inspired by mathematical forms or laws, such as sine-cosine functions and the golden ratio.

  • Music-based: Inspired by musical instruments and harmony search.

Difficulty Levels

This is an objective observation from the author based on the number of hyperparameters, mathematical complexity, original ideas, and Source Lines of Code (SLOC).

  • Easy: Few parameters, straightforward equations, and a very short SLOC.

  • Medium: More equations and a longer SLOC than the Easy level.

  • Hard: Lots of complex equations, extensive SLOC, and the foundational paper is difficult to read.

  • Hard* (Very Hard): Extremely complex equations, excessively long SLOC, and the paper is highly theoretical and challenging to parse.

Hint

Advice for Beginners

If you are new to the field of meta-heuristics, we strongly recommend starting your learning journey by reading papers and exploring source codes for algorithms categorized under the “Easy” or “Medium” difficulty levels.

Model References

For the complete and regularly updated list of references for all implemented models, please consult the References Section on our GitHub README.

License

MEALPY is open-source software and is officially released under the MIT License.