Citation Request

Please include these citations if you plan to use this library:

@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}
}

```

If you have an open-ended or a research question, you can contact me via nguyenthieu2102@gmail.com

Official Links

• Official source code repo: https://github.com/thieu1995/mealpy

• Official document: https://mealpy.readthedocs.io/

• Download releases: https://pypi.org/project/mealpy/

• Issue tracker: https://github.com/thieu1995/mealpy/issues

• Notable changes log: https://github.com/thieu1995/mealpy/blob/master/ChangeLog.md

• Examples with different meapy version: https://github.com/thieu1995/mealpy/blob/master/examples/EXAMPLES.md

• Official chat/support group: https://t.me/+fRVCJGuGJg1mNDg1

• This project also related to our another projects which are “meta-heuristics” and “machine learning”, check it here:

  • https://github.com/thieu1995/opfunu

  • https://github.com/thieu1995/metaheuristics

  • https://github.com/thieu1995/enoppy

  • https://github.com/thieu1995/MetaCluster

  • https://github.com/thieu1995/pfevaluator

  • https://github.com/mafese

  • https://github.com/permetrics

  • https://github.com/aiir-team

Classification Table

Warning: This classification is old. Now we update all new optimizers at this table.

• Meta-heuristic Algorithm’s Categories: (Based on this article)

  • Evolutionary-based: inspired by Darwin’s law of natural selection, evolutionary computing

  • Swarm-based: inspired by 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 multiverse

  • Human-based: inspired by human interaction, such as queuing search, teaching-learning, and cultural algorithms

  • Biology-based: inspired by biological creatures or microorganisms, such as genetic algorithms and artificial immune systems

  • System-based: inspired by ecosystem, immune system, and network system.

  • Math-based: inspired by mathematical forms or laws, such as sin-cosin functions, golden ratio.

  • Music-based: inspired by music instruments, such as harmony search

• Difficulty - Difficulty Level (Personal Opinion): Objective observation from author. Depend on the number of parameters, number of equations, the original ideas, time spend for coding, source lines of code (SLOC).

  • Easy: Few parameters, few equations, and very short SLOC (Source lines of code)

  • Medium: More equations than the Easy level, longer SLOC than the Easy level

  • Hard: Lots of equations, longer SLOC than the Medium level, and the paper is difficult to read.

  • Hard* - Very hard:Lots of equations, SLOC is too long, and the paper is very difficult to read.

For newbies, it is recommended to start by reading papers on algorithms that are categorized as “easy” or “medium” difficulty level.

📖 Please check the table from README.md on GitHub.

Model References

References

Please see the completed references list in file README.md in Github.

License

The project is licensed under MIT license.