mealpy 0.4.1

A collection of the state-of-the-art MEta-heuristics ALgorithms in PYthon (mealpy)

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

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

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

• This project also related to my another projects which are "meta-heuristics" and "neural-network", check it here

  • https://github.com/thieunguyen5991/metaheuristics
  • https://github.com/chasebk

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}
}

• 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.

• 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.

• 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

• Group:

  • Evolu: Evolutionary-based
  • Swarm: Swarm-based
  • Physi: Physics-based
  • Human: Human-based

• Levy: Using levy-flight technique or not

• Version:

  • original: Taken exactly from the paper
  • changed: I changed the flow or equation to make algorithm works

• 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

• 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

• 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

• 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