A collection of the state-of-the-art MEta-heuristics ALgorithms in PYthon (mealpy)
Project description
A collection of the state-of-the-art MEta-heuristics ALgorithms in PYthon (mealpy)
mealpy
mealpy is a python module for the most of cutting-edge population meta-heuristic algorithms and is distributed under MIT license.
Installation
Dependencies
- Python (>= 3.6)
- Numpy (>= 1.15.1)
User installation
Install the current PyPI release:
pip install mealpy
pip install --upgrade mealpy
Or install the development version from GitHub:
pip install git+https://github.com/thieunguyen5991/mealpy
Example
python examples/simple_run.py
The documentation includes more detailed installation instructions.
Changelog
- See the "ChangeLog.md" for a history of notable changes to mealpy.
Important links
-
Official source code repo: https://github.com/thieunguyen5991/mealpy
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Download releases: https://pypi.org/project/mealpy/
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Issue tracker: https://github.com/mealpy/mealpy/issues
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This project also related to my another projects which are "meta-heuristics" and "neural-network", check it here
Contributions
Citation
If you use mealpy in your project, I would appreciate citations:
@software{thieu_nguyen_2020_3711949,
author = {Thieu Nguyen},
title = {A collection of the state-of-the-art MEta-heuristics ALgorithms in PYthon: Mealpy},
month = march,
year = 2020,
publisher = {Zenodo},
doi = {10.5281/zenodo.3711948},
url = {https://doi.org/10.5281/zenodo.3711948}
}
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Nguyen, T., Nguyen, T., Nguyen, B. M., & Nguyen, G. (2019). Efficient Time-Series Forecasting Using Neural Network and Opposition-Based Coral Reefs Optimization. International Journal of Computational Intelligence Systems, 12(2), 1144-1161.
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Nguyen, T., Tran, N., Nguyen, B. M., & Nguyen, G. (2018, November). A Resource Usage Prediction System Using Functional-Link and Genetic Algorithm Neural Network for Multivariate Cloud Metrics. In 2018 IEEE 11th Conference on Service-Oriented Computing and Applications (SOCA) (pp. 49-56). IEEE.
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Nguyen, T., Nguyen, B. M., & Nguyen, G. (2019, April). Building Resource Auto-scaler with Functional-Link Neural Network and Adaptive Bacterial Foraging Optimization. In International Conference on Theory and Applications of Models of Computation (pp. 501-517). Springer, Cham.
Documents
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Group:
- Evolu: Evolutionary-based
- Swarm: Swarm-based
- Physi: Physics-based
- Human: Human-based
- Bio: Biology-based
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Levy: Using levy-flight technique or not
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Version:
- original: Taken exactly from the paper
- changed: I changed the flow or equation to make algorithm works
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Type:
- weak: working fine with uni-modal and some multi-modal functions
- strong: working good with uni-modal, multi-modal, some hybrid and some composite functions
- best: working well with almost all kind of functions
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Some algorithm with original version and no levy techniques still belong to the best type such as:
- Whale Optimization Algorithm
- Bird Swarm Algorithm
- Swarm Robotics Search And Rescue
- Manta Ray Foraging Optimization
- Henry Gas Solubility Optimization
- Atom Search Optimization
- Equilibrium Optimizer
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Paras: The number of parameters in the algorithm (Don't count the fixed number in the original paper)
- Almost algorithms has 2 (epoch, population_size) and plus some paras depend on each algorithm.
- Some algorithms belong to "best" type and have only 2 paras meaning that algorithm is very good
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Difficulty: 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: A few paras, few equations, SLOC very short
- Medium: more equations than Easy level, SLOC longer than Easy level
- Hard: Lots of equations, SLOC longer than Medium level, the paper hard to read.
- Very hard: Lots of equations, SLOC too long, the paper is very hard to read.
** For newbie, I recommend to read the paper of algorithms belong to "best or strong" type, "easy or medium" difficulty level.
| Group | STT | Name | Short | Year | Version | Levy | Type | Paras | Difficulty |
|---|---|---|---|---|---|---|---|---|---|
| Evolu | 1 | Genetic Algorithm | GA | 1992 | original | no | weak | 4 | easy |
| 2 | Differential Evolution | DE | 1997 | original | no | weak | 4 | easy | |
| 3 | |||||||||
| Swarm | 1 | Particle Swarm Optimization | PSO | 1995 | original | no | strong | 6 | easy |
| 2 | Bacterial Foraging Optimization | BFO | 2002 | orginal | no | weak | 11 | hard | |
| 3 | Cat Swarm Optimization | CSO | 2006 | original | no | weak | 9 | hard | |
| 4 | Artificial Bee Colony | ABC | 2007 | changed | no | strong | 6 | easy | |
| 5 | Bat Algorithm | BA | 2010 | original | no | weak | 5 | easy | |
| 6 | Social Spider Optimization | SSO | 2013 | changed | no | weak | 3 | very hard | |
| 7 | Grey Wolf Optimizer | GWO | 2014 | original | no | strong | 2 | easy | |
| 8 | Social Spider Algorithm | SSA | 2015 | original | no | strong | 5 | easy | |
| 9 | Ant Lion Optimizer | ALO | 2015 | original | no | weak | 2 | medium | |
| 10 | Moth Flame Optimization | MFO | 2015 | changed | no | strong | 2 | easy | |
| 11 | Whale Optimization Algorithm | WOA | 2016 | original | no | best | 2 | easy | |
| 12 | Bird Swarm Algorithm | BSA | 2016 | original | no | best | 9 | medium | |
| 13 | Swarm Robotics Search And Rescue | SRSR | 2017 | original | no | best | 2 | very hard | |
| 14 | Grasshopper Optimisation Algorithm | GOA | 2017 | original | no | weak | 3 | easy | |
| 15 | Earthworm Optimisation Algorithm | EOA | 2018 | original | no | weak | 8 | medium | |
| 16 | Moth Search Algorithm | MSA | 2018 | changed | no | weak | 5 | easy | |
| 17 | Rhino Herd Optimization | RHO | 2018 | original | no | weak | 6 | easy | |
| 18 | Emperor Penguin Optimizer | EPO | 2018 | changed | no | strong | 2 | easy | |
| 19 | Nake Mole-rat Algorithm | NMRA | 2019 | original | no | strong | 3 | easy | |
| 20 | Bald Eagle Search | BES | 2019 | changed | no | best | 7 | medium | |
| 21 | Pathfinder Algorithm | PFA | 2019 | original | no | strong | 2 | easy | |
| 22 | Sailfish Optimizer | SFO | 2019 | original | no | strong | 5 | medium | |
| 23 | Harris Hawks Optimization | HHO | 2019 | original | yes | best | 2 | medium | |
| 24 | Manta Ray Foraging Optimization | MRFO | 2020 | original | no | best | 3 | easy | |
| 25 | |||||||||
| Physi | 1 | Wind Driven Optimization | WDO | 2013 | original | no | strong | 7 | easy |
| 2 | Multi-Verse Optimizer | MVO | 2016 | changed | no | strong | 3 | easy | |
| 3 | Tug of War Optimization | TWO | 2016 | original | no | strong | 2 | easy | |
| 4 | Electromagnetic Field Optimization | EFO | 2016 | original | no | strong | 6 | easy | |
| 5 | Nuclear Reaction Optimization | NRO | 2019 | original | yes | best | 2 | very hard | |
| 6 | Henry Gas Solubility Optimization | HGSO | 2019 | original | no | best | 3 | medium | |
| 7 | Atom Search Optimization | ASO | 2019 | original | no | best | 4 | medium | |
| 8 | Equilibrium Optimizer | EO | 2019 | original | no | best | 2 | easy | |
| 9 | |||||||||
| Human | 1 | Teaching Learning Optimization | TLO | 2011 | original | no | strong | 2 | easy |
| 2 | Queuing Search Algorithm | QSA | 2019 | original | no | strong | 2 | hard | |
| 3 | Search And Rescue Optimization | SARO | 2019 | original | no | strong | 4 | medium | |
| 4 | Life Choice-Based Optimization | LCBO | 2019 | original | no | strong | 2 | easy | |
| 5 | Social Ski-Driver Optimization | SSDO | 2019 | changed | no | BEST | 2 | easy | |
| 6 | |||||||||
| Bio | 1 | Invasive Weed Optimization | IWO | 2006 | original | no | strong | 5 | easy |
| 2 | Biogeography-Based Optimization | BBO | 2008 | original | no | strong | 4 | easy | |
| 3 |
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