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

"Knowledge is power, sharing it is the premise of progress in life. It seems like a burden to someone, but it is the only way to achieve immortality." --- Thieu Nguyen

Introduction

• MEALPY is a python module for the most of cutting-edge population meta-heuristic algorithms and is distributed under MIT license.

• The goals of this framework are:

  • Sharing knowledge of meta-heuristic fields to everyone without a fee
  • Helping other researchers in all field access to optimization algorithms as quickly as possible
  • Implement the classical as well as the state-of-the-art meta-heuristics (The whole history of meta-heuristics)

Installation

Dependencies

• Python (>= 3.6)
• Numpy (>= 1.15.1)
• Scikit-learn (>= 0.22.1)
• Matplotlib (>=3.1.3)
• Opfunu (>= 0.4.3)

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/thieunguyen5991/mealpy/issues

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

  • https://github.com/thieunguyen5991/opfunu
  • 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}
}

Documents

• Group:

  • Evolu: Evolutionary-based
  • Swarm: Swarm-based
  • Physic: Physics-based
  • Human: Human-based
  • Bio: Biology-based
  • System: System-based (eco-system, immune-system, network-system, ...)
  • Math: Math-based
  • Music: Music-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
  • BEST: the best among all algorithms

• 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
  • Artificial Ecosystem-based Optimization

• Paras: The number of parameters in the algorithm (Not counting the fixed parameters in the original paper)

  • Almost algorithms have 2 paras (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 outstanding

• Diffic - Difficulty Level: 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.
  • Hard* - 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.

A

• ALO - Ant Lion Optimizer . Mirjalili S (2015). “The Ant Lion Optimizer.” Advances in Engineering Software, 83, 80-98. doi: 10.1016/j.advengsoft.2015.01.010

• ABC - Artificial Bee Colony . Karaboga, D. (2005). An idea based on honey bee swarm for numerical optimization (Vol. 200, pp. 1-10). Technical report-tr06, Erciyes university, engineering faculty, computer engineering department.

• AEO - Artificial Ecosystem-based Optimization . Zhao, W., Wang, L., & Zhang, Z. (2019). Artificial ecosystem-based optimization: a novel nature-inspired meta-heuristic algorithm. Neural Computing and Applications, 1-43.

• ASO - Atom Search Optimization . Zhao, W., Wang, L., & Zhang, Z. (2019). Atom search optimization and its application to solve a hydrogeologic parameter estimation problem. Knowledge-Based Systems, 163, 283-304.

B

• BFO - Bacterial Foraging Optimization . Passino, K. M. (2002). Biomimicry of bacterial foraging for distributed optimization and control. IEEE control systems magazine, 22(3), 52-67.

• BES - Bald Eagle Search . Alsattar, H. A., Zaidan, A. A., & Zaidan, B. B. (2019). Novel meta-heuristic bald eagle search optimisation algorithm. Artificial Intelligence Review, 1-28.

• BA - Bat Algorithm . Yang, X. S. (2010). A new metaheuristic bat-inspired algorithm. In Nature inspired cooperative strategies for optimization (NICSO 2010) (pp. 65-74). Springer, Berlin, Heidelberg.

• BBO - Biogeography-Based Optimization . Simon, D. (2008). Biogeography-based optimization. IEEE transactions on evolutionary computation, 12(6), 702-713.

• BSA - Bird Swarm Algorithm . Meng, X. B., Gao, X. Z., Lu, L., Liu, Y., & Zhang, H. (2016). A new bio-inspired optimisation algorithm: Bird Swarm Algorithm. Journal of Experimental & Theoretical Artificial Intelligence, 28(4), 673-687.

• BWO - Black Widow Optimization . Hayyolalam, V., & Kazem, A. A. P. (2020). Black Widow Optimization Algorithm: A novel meta-heuristic approach for solving engineering optimization problems. Engineering Applications of Artificial Intelligence, 87, 103249.

C

• CSO - Cat Swarm Optimization . Chu, S. C., Tsai, P. W., & Pan, J. S. (2006, August). Cat swarm optimization. In Pacific Rim international conference on artificial intelligence (pp. 854-858). Springer, Berlin, Heidelberg.

D

• DE - Differential Evolution . Storn, R., & Price, K. (1997). Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of global optimization, 11(4), 341-359.

E

• EOA - Earthworm Optimisation Algorithm . Wang, G. G., Deb, S., & dos Santos Coelho, L. (2018). Earthworm optimisation algorithm: a bio-inspired metaheuristic algorithm for global optimisation problems. IJBIC, 12(1), 1-22.
• EFO - Electromagnetic Field Optimization . Abedinpourshotorban, H., Shamsuddin, S. M., Beheshti, Z., & Jawawi, D. N. (2016). Electromagnetic field optimization: A physics-inspired metaheuristic optimization algorithm. Swarm and Evolutionary Computation, 26, 8-22.
• EPO - Emperor Penguin Optimizer . Dhiman, G., & Kumar, V. (2018). Emperor penguin optimizer: A bio-inspired algorithm for engineering problems. Knowledge-Based Systems, 159, 20-50.
• EO - Equilibrium Optimizer . Faramarzi, A., Heidarinejad, M., Stephens, B., & Mirjalili, S. (2019). Equilibrium optimizer: A novel optimization algorithm. Knowledge-Based Systems.

F

G

• GA - Genetic Algorithm . Holland, J. H. (1992). Genetic algorithms. Scientific american, 267(1), 66-73.
• GCO - Germinal Center Optimization . Villaseñor, C., Arana-Daniel, N., Alanis, A. Y., López-Franco, C., & Hernandez-Vargas, E. A. (2018). Germinal center optimization algorithm. International Journal of Computational Intelligence Systems, 12(1), 13-27.
• GOA - Grasshopper Optimisation Algorithm . Saremi, S., Mirjalili, S., & Lewis, A. (2017). Grasshopper optimisation algorithm: theory and application. Advances in Engineering Software, 105, 30-47.
• GWO - Grey Wolf Optimizer . Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in engineering software, 69, 46-61.

H

• HS - Harmony Search . Geem, Z. W., Kim, J. H., & Loganathan, G. V. (2001). A new heuristic optimization algorithm: harmony search. simulation, 76(2), 60-68.
• HHO - Harris Hawks Optimization . Heidari, A. A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., & Chen, H. (2019). Harris hawks optimization: Algorithm and applications. Future Generation Computer Systems, 97, 849-872.
• HGSO - Henry Gas Solubility Optimization . Hashim, F. A., Houssein, E. H., Mabrouk, M. S., Al-Atabany, W., & Mirjalili, S. (2019). Henry gas solubility optimization: A novel physics-based algorithm. Future Generation Computer Systems, 101, 646-667.

I

• IWO - Invasive Weed Optimization . Mehrabian, A. R., & Lucas, C. (2006). A novel numerical optimization algorithm inspired from weed colonization. Ecological informatics, 1(4), 355-366.

J

K

L

• LCBO - Life Choice-Based Optimization . Khatri, A., Gaba, A., Rana, K. P. S., & Kumar, V. (2019). A novel life choice-based optimizer. Soft Computing, 1-21.

M

• MRFO - Manta Ray Foraging Optimization . Zhao, W., Zhang, Z., & Wang, L. (2020). Manta ray foraging optimization: An effective bio-inspired optimizer for engineering applications. Engineering Applications of Artificial Intelligence, 87, 103300.
• MFO - Moth Flame Optimization . Mirjalili, S. (2015). Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-based systems, 89, 228-249.
• MSA - Moth Search Algorithm . Wang, G. G. (2018). Moth search algorithm: a bio-inspired metaheuristic algorithm for global optimization problems. Memetic Computing, 10(2), 151-164.
• MVO - Multi-Verse Optimizer . Mirjalili, S., Mirjalili, S. M., & Hatamlou, A. (2016). Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Computing and Applications, 27(2), 495-513.
• NMRA - Nake Mole-rat Algorithm . Salgotra, R., & Singh, U. (2019). The naked mole-rat algorithm. Neural Computing and Applications, 31(12), 8837-8857.

N

• NRO - Nuclear Reaction Optimization . Wei, Z., Huang, C., Wang, X., Han, T., & Li, Y. (2019). Nuclear Reaction Optimization: A novel and powerful physics-based algorithm for global optimization. IEEE Access.

O

P

• PSO - Particle Swarm Optimization . Eberhart, R., & Kennedy, J. (1995, October). A new optimizer using particle swarm theory. In MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science (pp. 39-43). Ieee.
• PFA - Pathfinder Algorithm . Yapici, H., & Cetinkaya, N. (2019). A new meta-heuristic optimizer: Pathfinder algorithm. Applied Soft Computing, 78, 545-568.

Q

• QSA - Queuing Search Algorithm . Zhang, J., Xiao, M., Gao, L., & Pan, Q. (2018). Queuing search algorithm: A novel metaheuristic algorithm for solving engineering optimization problems. Applied Mathematical Modelling, 63, 464-490.

R

• RHO - Rhino Herd Optimization . Wang, G. G., Gao, X. Z., Zenger, K., & Coelho, L. D. S. (2018, December). A novel metaheuristic algorithm inspired by rhino herd behavior. In Proceedings of The 9th EUROSIM Congress on Modelling and Simulation, EUROSIM 2016, The 57th SIMS Conference on Simulation and Modelling SIMS 2016 (No. 142, pp. 1026-1033). Linköping University Electronic Press.

S

• SFO - Sailfish Optimizer . Shadravan, S., Naji, H. R., & Bardsiri, V. K. (2019). The Sailfish Optimizer: A novel nature-inspired metaheuristic algorithm for solving constrained engineering optimization problems. Engineering Applications of Artificial Intelligence, 80, 20-34.
• SBO - Satin Bowerbird Optimizer . Moosavi, S. H. S., & Bardsiri, V. K. (2017). Satin bowerbird optimizer: a new optimization algorithm to optimize ANFIS for software development effort estimation. Engineering Applications of Artificial Intelligence, 60, 1-15.
• SARO - Search And Rescue Optimization . Shabani, A., Asgarian, B., Gharebaghi, S. A., Salido, M. A., & Giret, A. (2019). A New Optimization Algorithm Based on Search and Rescue Operations. Mathematical Problems in Engineering, 2019.
• SCA - Sine Cosine Algorithm . Mirjalili, S. (2016). SCA: a sine cosine algorithm for solving optimization problems. Knowledge-Based Systems, 96, 120-133.
• SSDO - Social Ski-Driver Optimization . Tharwat, A., & Gabel, T. (2019). Parameters optimization of support vector machines for imbalanced data using social ski driver algorithm. Neural Computing and Applications, 1-14.
• SSA - Social Spider Algorithm . James, J. Q., & Li, V. O. (2015). A social spider algorithm for global optimization. Applied Soft Computing, 30, 614-627.
• SSO - Social Spider Optimization . Cuevas, E., Cienfuegos, M., ZaldíVar, D., & Pérez-Cisneros, M. (2013). A swarm optimization algorithm inspired in the behavior of the social-spider. Expert Systems with Applications, 40(16), 6374-6384.
• SRSR - Swarm Robotics Search And Rescue . Bakhshipour, M., Ghadi, M. J., & Namdari, F. (2017). Swarm robotics search & rescue: A novel artificial intelligence-inspired optimization approach. Applied Soft Computing, 57, 708-726.

T

• TLO - Teaching Learning Optimization . Rao, R. V., Savsani, V. J., & Vakharia, D. P. (2011). Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-Aided Design, 43(3), 303-315.
• TWO - Tug of War Optimization . Kaveh, A., & Zolghadr, A. (2016). A novel meta-heuristic algorithm: tug of war optimization. Iran University of Science & Technology, 6(4), 469-492.

U

V

W

X

• WOA - Whale Optimization Algorithm . Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in engineering software, 95, 51-67.
• WHO - Wildebeest Herd Optimization . Amali, D., & Dinakaran, M. (2019). Wildebeest herd optimization: A new global optimization algorithm inspired by wildebeest herding behaviour. Journal of Intelligent & Fuzzy Systems, (Preprint), 1-14.
• WDO - Wind Driven Optimization . Bayraktar, Z., Komurcu, M., & Werner, D. H. (2010, July). Wind Driven Optimization (WDO): A novel nature-inspired optimization algorithm and its application to electromagnetics. In 2010 IEEE antennas and propagation society international symposium (pp. 1-4). IEEE.

Y

Z