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

Classification 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.

Group

Name

Module

Class

Year

Paras

Difficulty

Evolutionary

Evolutionary Programming

EP

OriginalEP

1964

3

easy

Evolutionary

LevyEP

3

easy

Evolutionary

Evolution Strategies

ES

OriginalES

1971

3

easy

Evolutionary

LevyES

3

easy

Evolutionary

CMA_ES

2003

2

hard

Evolutionary

Simple_CMA_ES

2023

2

medium

Evolutionary

Memetic Algorithm

MA

OriginalMA

1989

7

easy

Evolutionary

Genetic Algorithm

GA

BaseGA

1992

4

easy

Evolutionary

SingleGA

7

easy

Evolutionary

MultiGA

7

easy

Evolutionary

EliteSingleGA

10

easy

Evolutionary

EliteMultiGA

10

easy

Evolutionary

Differential Evolution

DE

BaseDE

1997

5

easy

Evolutionary

JADE

2009

6

medium

Evolutionary

SADE

2005

2

medium

Evolutionary

SAP_DE

2006

3

medium

Evolutionary

Success-History Adaptation Differential Evolution

SHADE

OriginalSHADE

2013

4

medium

Evolutionary

L_SHADE

2014

4

medium

Evolutionary

Flower Pollination Algorithm

FPA

OriginalFPA

2014

4

medium

Evolutionary

Coral Reefs Optimization

CRO

OriginalCRO

2014

11

medium

Evolutionary

OCRO

2019

12

medium

***

***

***

***

***

***

***

Swarm

Particle Swarm Optimization

PSO

OriginalPSO

1995

6

easy

Swarm

PPSO

2019

2

medium

Swarm

HPSO_TVAC

2017

4

medium

Swarm

C_PSO

2015

6

medium

Swarm

CL_PSO

2006

6

medium

Swarm

Bacterial Foraging Optimization

BFO

OriginalBFO

2002

10

hard

Swarm

ABFO

2019

8

medium

Swarm

Bees Algorithm

BeesA

OriginalBeesA

2005

8

medium

Swarm

ProbBeesA

2015

5

medium

Swarm

CleverBookBeesA

2006

8

medium

Swarm

Cat Swarm Optimization

CSO

OriginalCSO

2006

11

hard

Swarm

Artificial Bee Colony

ABC

OriginalABC

2007

8

medium

Swarm

Ant Colony Optimization

ACOR

OriginalACOR

2008

5

easy

Swarm

Cuckoo Search Algorithm

CSA

OriginalCSA

2009

3

medium

Swarm

Firefly Algorithm

FFA

OriginalFFA

2009

8

easy

Swarm

Fireworks Algorithm

FA

OriginalFA

2010

7

medium

Swarm

Bat Algorithm

BA

OriginalBA

2010

6

medium

Swarm

AdaptiveBA

2010

8

medium

Swarm

ModifiedBA

5

medium

Swarm

Fruit-fly Optimization Algorithm

FOA

OriginalFOA

2012

2

easy

Swarm

BaseFOA

2

easy

Swarm

WhaleFOA

2020

2

medium

Swarm

Social Spider Optimization

SSpiderO

OriginalSSpiderO

2018

4

hard*

Swarm

Grey Wolf Optimizer

GWO

OriginalGWO

2014

2

easy

Swarm

RW_GWO

2019

2

easy

Swarm

Social Spider Algorithm

SSpiderA

OriginalSSpiderA

2015

5

medium

Swarm

Ant Lion Optimizer

ALO

OriginalALO

2015

2

easy

Swarm

BaseALO

2

easy

Swarm

Moth Flame Optimization

MFO

OriginalMFO

2015

2

easy

Swarm

BaseMFO

2

easy

Swarm

Elephant Herding Optimization

EHO

OriginalEHO

2015

5

easy

Swarm

Jaya Algorithm

JA

OriginalJA

2016

2

easy

Swarm

BaseJA

2

easy

Swarm

LevyJA

2021

2

easy

Swarm

Whale Optimization Algorithm

WOA

OriginalWOA

2016

2

medium

Swarm

HI_WOA

2019

3

medium

Swarm

Dragonfly Optimization

DO

OriginalDO

2016

2

medium

Swarm

Bird Swarm Algorithm

BSA

OriginalBSA

2016

9

medium

Swarm

Spotted Hyena Optimizer

SHO

OriginalSHO

2017

4

medium

Swarm

Salp Swarm Optimization

SSO

OriginalSSO

2017

2

easy

Swarm

Swarm Robotics Search And Rescue

SRSR

OriginalSRSR

2017

2

hard*

Swarm

Grasshopper Optimisation Algorithm

GOA

OriginalGOA

2017

4

easy

Swarm

Coyote Optimization Algorithm

COA

OriginalCOA

2018

3

medium

Swarm

Moth Search Algorithm

MSA

OriginalMSA

2018

5

easy

Swarm

Sea Lion Optimization

SLO

OriginalSLO

2019

2

medium

Swarm

ModifiedSLO

2

medium

Swarm

ImprovedSLO

2022

4

medium

Swarm

Nake Mole*Rat Algorithm

NMRA

OriginalNMRA

2019

3

easy

Swarm

ImprovedNMRA

4

medium

Swarm

Pathfinder Algorithm

PFA

OriginalPFA

2019

2

medium

Swarm

Sailfish Optimizer

SFO

OriginalSFO

2019

5

easy

Swarm

ImprovedSFO

3

medium

Swarm

Harris Hawks Optimization

HHO

OriginalHHO

2019

2

medium

Swarm

Manta Ray Foraging Optimization

MRFO

OriginalMRFO

2020

3

medium

Swarm

Bald Eagle Search

BES

OriginalBES

2020

7

easy

Swarm

Sparrow Search Algorithm

SSA

OriginalSSA

2020

5

medium

Swarm

BaseSSA

5

medium

Swarm

Hunger Games Search

HGS

OriginalHGS

2021

4

medium

Swarm

Aquila Optimizer

AO

OriginalAO

2021

2

easy

Swarm

Hybrid Grey Wolf * Whale Optimization Algorithm

GWO

GWO_WOA

2022

2

easy

Swarm

Marine Predators Algorithm

MPA

OriginalMPA

2020

2

medium

Swarm

Honey Badger Algorithm

HBA

OriginalHBA

2022

2

easy

Swarm

Sand Cat Swarm Optimization

SCSO

OriginalSCSO

2022

2

easy

Swarm

Tuna Swarm Optimization

TSO

OriginalTSO

2021

2

medium

Swarm

African Vultures Optimization Algorithm

AVOA

OriginalAVOA

2022

7

medium

Swarm

Artificial Gorilla Troops Optimization

AGTO

OriginalAGTO

2021

5

medium

Swarm

MGTO

2023

3

medium

Swarm

Artificial Rabbits Optimization

ARO

OriginalARO

2022

2

easy

Swarm

LARO

2022

2

easy

Swarm

IARO

2022

2

easy

Swarm

Egret Swarm Optimization Algorithm

ESOA

OriginalESOA

2022

2

medium

Swarm

Fox Optimizer

FOX

OriginalFOX

2023

4

easy

Swarm

Golden Jackal Optimization

GJO

OriginalGJO

2022

2

easy

Swarm

Giant Trevally Optimization

GTO

OriginalGTO

2022

4

medium

Swarm

Matlab101GTO

2022

2

medium

Swarm

Matlab102GTO

2023

2

hard

Swarm

Mountain Gazelle Optimizer

MGO

OriginalMGO

2022

2

easy

Swarm

Sea-Horse Optimization

SeaHO

OriginalSeaHO

2022

2

medium

***

***

***

***

***

***

***

Physics

Simulated Annealling

SA

OriginalSA

1983

9

medium

Physics

GaussianSA

5

medium

Physics

SwarmSA

1987

9

medium

Physics

Wind Driven Optimization

WDO

OriginalWDO

2013

7

easy

Physics

Multi*Verse Optimizer

MVO

OriginalMVO

2016

4

easy

Physics

BaseMVO

4

easy

Physics

Tug of War Optimization

TWO

OriginalTWO

2016

2

easy

Physics

OppoTWO

2

medium

Physics

LevyTWO

2

medium

Physics

EnhancedTWO

2020

2

medium

Physics

Electromagnetic Field Optimization

EFO

OriginalEFO

2016

6

easy

Physics

BaseEFO

6

medium

Physics

Nuclear Reaction Optimization

NRO

OriginalNRO

2019

2

hard*

Physics

Henry Gas Solubility Optimization

HGSO

OriginalHGSO

2019

3

medium

Physics

Atom Search Optimization

ASO

OriginalASO

2019

4

medium

Physics

Equilibrium Optimizer

EO

OriginalEO

2019

2

easy

Physics

ModifiedEO

2020

2

medium

Physics

AdaptiveEO

2020

2

medium

Physics

Archimedes Optimization Algorithm

ArchOA

OriginalArchOA

2021

8

medium

Physics

Chernobyl Disaster Optimization

CDO

OriginalCDO

2023

2

easy

Physics

Energy Valley Optimization

EVO

OriginalEVO

2023

2

medium

Physics

Fick’s Law Algorithm

FLA

OriginalFLA

2023

8

hard

Physics

Physical Phenomenon of RIME-ice

RIME

OriginalRIME

2023

3

easy

***

***

***

***

***

***

***

Human

Culture Algorithm

CA

OriginalCA

1994

3

easy

Human

Imperialist Competitive Algorithm

ICA

OriginalICA

2007

8

hard*

Human

Teaching Learning*based Optimization

TLO

OriginalTLO

2011

2

easy

Human

BaseTLO

2012

2

easy

Human

ITLO

2013

3

medium

Human

Brain Storm Optimization

BSO

OriginalBSO

2011

8

medium

Human

ImprovedBSO

2017

7

medium

Human

Queuing Search Algorithm

QSA

OriginalQSA

2019

2

hard

Human

BaseQSA

2

hard

Human

OppoQSA

2

hard

Human

LevyQSA

2

hard

Human

ImprovedQSA

2021

2

hard

Human

Search And Rescue Optimization

SARO

OriginalSARO

2019

4

medium

Human

BaseSARO

4

medium

Human

Life Choice*Based Optimization

LCO

OriginalLCO

2019

3

easy

Human

BaseLCO

3

easy

Human

ImprovedLCO

2

easy

Human

Social Ski*Driver Optimization

SSDO

OriginalSSDO

2019

2

easy

Human

Gaining Sharing Knowledge*based Algorithm

GSKA

OriginalGSKA

2019

6

medium

Human

BaseGSKA

4

medium

Human

Coronavirus Herd Immunity Optimization

CHIO

OriginalCHIO

2020

4

medium

Human

BaseCHIO

4

medium

Human

Forensic*Based Investigation Optimization

FBIO

OriginalFBIO

2020

2

medium

Human

BaseFBIO

2

medium

Human

Battle Royale Optimization

BRO

OriginalBRO

2020

3

medium

Human

BaseBRO

3

medium

Human

Student Psychology Based Optimization

SPBO

OriginalSPBO

2020

2

medium

Human

DevSPBO

2

medium

Human

Heap-based Optimization

HBO

OriginalHBO

2020

3

medium

Human

Human Conception Optimization

HCO

OriginalHCO

2022

6

medium

Human

Dwarf Mongoose Optimization Algorithm

DMOA

OriginalDMOA

2022

4

medium

Human

DevDMOA

3

medium

Human

War Strategy Optimization

WarSO

OriginalWarSO

2022

3

easy

***

***

***

***

***

***

***

Bio

Invasive Weed Optimization

IWO

OriginalIWO

2006

7

easy

Bio

Biogeography*Based Optimization

BBO

OriginalBBO

2008

4

easy

Bio

BaseBBO

4

easy

Bio

Virus Colony Search

VCS

OriginalVCS

2016

4

hard*

Bio

BaseVCS

4

hard*

Bio

Satin Bowerbird Optimizer

SBO

OriginalSBO

2017

5

easy

Bio

BaseSBO

5

easy

Bio

Earthworm Optimisation Algorithm

EOA

OriginalEOA

2018

8

medium

Bio

Wildebeest Herd Optimization

WHO

OriginalWHO

2019

12

hard

Bio

Slime Mould Algorithm

SMA

OriginalSMA

2020

3

easy

Bio

BaseSMA

3

easy

Bio

Barnacles Mating Optimizer

BMO

OriginalBMO

2018

3

easy

Bio

Tunicate Swarm Algorithm

TSA

OriginalTSA

2020

2

easy

Bio

Symbiotic Organisms Search

SOS

OriginalSOS

2014

2

medium

Bio

Seagull Optimization Algorithm

SOA

OriginalSOA

2019

3

easy

Bio

DevSOA

3

easy

Bio

Brown-Bear Optimization Algorithm

BBOA

OriginalBBOA

2023

2

medium

Bio

Tree Physiology Optimization

TPO

OriginalTPO

2017

5

medium

***

***

***

***

***

***

***

System

Germinal Center Optimization

GCO

OriginalGCO

2018

4

medium

System

BaseGCO

4

medium

System

Water Cycle Algorithm

WCA

OriginalWCA

2012

5

medium

System

Artificial Ecosystem*based Optimization

AEO

OriginalAEO

2019

2

easy

System

EnhancedAEO

2020

2

medium

System

ModifiedAEO

2020

2

medium

System

ImprovedAEO

2021

2

medium

System

AugmentedAEO

2022

2

medium

***

***

***

***

***

***

***

Math

Hill Climbing

HC

OriginalHC

1993

3

easy

Math

SwarmHC

3

easy

Math

Cross-Entropy Method

CEM

OriginalCEM

1997

4

easy

Math

Tabu Search

TS

OriginalTS

2004

5

easy

Math

Sine Cosine Algorithm

SCA

OriginalSCA

2016

2

easy

Math

BaseSCA

2

easy

Math

QLE-SCA

2022

4

hard

Math

Gradient-Based Optimizer

GBO

OriginalGBO

2020

5

medium

Math

Arithmetic Optimization Algorithm

AOA

OrginalAOA

2021

6

easy

Math

Chaos Game Optimization

CGO

OriginalCGO

2021

2

easy

Math

Pareto-like Sequential Sampling

PSS

OriginalPSS

2021

4

medium

Math

weIghted meaN oF vectOrs

INFO

OriginalINFO

2022

2

medium

Math

RUNge Kutta optimizer

RUN

OriginalRUN

2021

2

hard

Math

Circle Search Algorithm

CircleSA

OriginalCircleSA

2022

3

easy

Math

Success History Intelligent Optimization

SHIO

OriginalSHIO

2022

2

easy

***

***

***

***

***

***

***

Music

Harmony Search

HS

OriginalHS

2001

4

easy

Music

BaseHS

4

easy

+++

+++

+++

+++

+++

+++

+++

WARNING

PLEASE CHECK PLAGIARISM BEFORE USING BELOW ALGORITHMS

Swarm

Coati Optimization Algorithm

CoatiOA

OriginalCoatiOA

2023

2

easy

Swarm

Fennec For Optimization

FFO

OriginalFFO

2022

2

easy

Swarm

Northern Goshawk Optimization

NGO

OriginalNGO

2021

2

easy

Swarm

Osprey Optimization Algorithm

OOA

OriginalOOA

2023

2

easy

Swarm

Pelican Optimization Algorithm

POA

OriginalPOA

2023

2

easy

Swarm

Serval Optimization Algorithm

ServalOA

OriginalServalOA

2022

2

easy

Swarm

Siberian Tiger Optimization

STO

OriginalSTO

2022

2

easy

Swarm

Tasmanian Devil Optimization

TDO

OriginalTDO

2022

2

easy

Swarm

Walrus Optimization Algorithm

WaOA

OriginalWaOA

2022

2

easy

Swarm

Zebra Optimization Algorithm

ZOA

OriginalZOA

2022

2

easy

Human

Teamwork Optimization Algorithm

TOA

OriginalTOA

2021

2

easy

Model References

A.

  • ABC - Artificial Bee Colony * OriginalABC: 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.

  • ACOR - Ant Colony Optimization. * OriginalACOR: Socha, K., & Dorigo, M. (2008). Ant colony optimization for continuous domains. European journal of operational research, 185(3), 1155-1173.

  • ALO - Ant Lion Optimizer * OriginalALO: Mirjalili S (2015). “The Ant Lion Optimizer.” Advances in Engineering Software, 83, 80-98. doi: [10.1016/j.advengsoft.2015.01.010](https://doi.org/10.1016/j.advengsoft.2015.01.010) * BaseALO: The developed version

  • AEO - Artificial Ecosystem-based Optimization * OriginalAEO: Zhao, W., Wang, L., & Zhang, Z. (2019). Artificial ecosystem-based optimization: a novel nature-inspired meta-heuristic algorithm. Neural Computing and Applications, 1-43. * AugmentedAEO: Van Thieu, N., Barma, S. D., Van Lam, T., Kisi, O., & Mahesha, A. (2022). Groundwater level modeling using Augmented Artificial Ecosystem Optimization. Journal of Hydrology, 129034. * ImprovedAEO: Rizk-Allah, R. M., & El-Fergany, A. A. (2020). Artificial ecosystem optimizer for parameters identification of proton exchange membrane fuel cells model. International Journal of Hydrogen Energy. * EnhancedAEO: Eid, A., Kamel, S., Korashy, A., & Khurshaid, T. (2020). An Enhanced Artificial Ecosystem-Based Optimization for Optimal Allocation of Multiple Distributed Generations. IEEE Access, 8, 178493-178513. * ModifiedAEO: Menesy, A. S., Sultan, H. M., Korashy, A., Banakhr, F. A., Ashmawy, M. G., & Kamel, S. (2020). Effective parameter extraction of different polymer electrolyte membrane fuel cell stack models using a modified artificial ecosystem optimization algorithm. IEEE Access, 8, 31892-31909.

  • ASO - Atom Search Optimization * OriginalASO: 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.

  • ArchOA - Archimedes Optimization Algorithm * OriginalArchOA: Hashim, F. A., Hussain, K., Houssein, E. H., Mabrouk, M. S., & Al-Atabany, W. (2021). Archimedes optimization algorithm: a new metaheuristic algorithm for solving optimization problems. Applied Intelligence, 51(3), 1531-1551.

  • AOA - Arithmetic Optimization Algorithm * OriginalAOA: Abualigah, L., Diabat, A., Mirjalili, S., Abd Elaziz, M., & Gandomi, A. H. (2021). The arithmetic optimization algorithm. Computer methods in applied mechanics and engineering, 376, 113609.

  • AO - Aquila Optimizer * OriginalAO: Abualigah, L., Yousri, D., Abd Elaziz, M., Ewees, A. A., Al-qaness, M. A., & Gandomi, A. H. (2021). Aquila Optimizer: A novel meta-heuristic optimization Algorithm. Computers & Industrial Engineering, 157, 107250.

B.

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

  • BeesA - Bees Algorithm * OriginalBeesA: Pham, D. T., Ghanbarzadeh, A., Koc, E., Otri, S., Rahim, S., & Zaidi, M. (2005). The bees algorithm. Technical Note, Manufacturing Engineering Centre, Cardiff University, UK. * ProbBeesA: The probabilitic version of: Pham, D. T., Ghanbarzadeh, A., Koç, E., Otri, S., Rahim, S., & Zaidi, M. (2006). The bees algorithm—a novel tool for complex optimisation problems. In Intelligent production machines and systems (pp. 454-459). Elsevier Science Ltd.

  • BBO - Biogeography-Based Optimization * OriginalBBO: Simon, D. (2008). Biogeography-based optimization. IEEE transactions on evolutionary computation, 12(6), 702-713. * BaseBBO: The developed version

  • BA - Bat Algorithm * OriginalBA: 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. * AdaptiveBA: Wang, X., Wang, W. and Wang, Y., 2013, July. An adaptive bat algorithm. In International Conference on Intelligent Computing(pp. 216-223). Springer, Berlin, Heidelberg. * ModifiedBA: Dong, H., Li, T., Ding, R. and Sun, J., 2018. A novel hybrid genetic algorithm with granular information for feature selection and optimization. Applied Soft Computing, 65, pp.33-46.

  • BSO - Brain Storm Optimization * OriginalBSO: . Shi, Y. (2011, June). Brain storm optimization algorithm. In International conference in swarm intelligence (pp. 303-309). Springer, Berlin, Heidelberg. * ImprovedBSO: El-Abd, M., 2017. Global-best brain storm optimization algorithm. Swarm and evolutionary computation, 37, pp.27-44.

  • BSA - Bird Swarm Algorithm * OriginalBSA: 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.

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

  • BRO - Battle Royale Optimization * OriginalBRO: Rahkar Farshi, T. (2020). Battle royale optimization algorithm. Neural Computing and Applications, 1-19. * BaseBRO: The developed version

C.

  • CA - Culture Algorithm * OriginalCA: Reynolds, R.G., 1994, February. An introduction to cultural algorithms. In Proceedings of the third annual conference on evolutionary programming (Vol. 24, pp. 131-139). River Edge, NJ: World Scientific.

  • CEM - Cross Entropy Method * OriginalCEM: Rubinstein, R. (1999). The cross-entropy method for combinatorial and continuous optimization. Methodology and computing in applied probability, 1(2), 127-190.

  • CSO - Cat Swarm Optimization * OriginalCSO: 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.

  • CSA - Cuckoo Search Algorithm * OriginalCSA: Yang, X. S., & Deb, S. (2009, December). Cuckoo search via Lévy flights. In 2009 World congress on nature & biologically inspired computing (NaBIC) (pp. 210-214). Ieee.

  • CRO - Coral Reefs Optimization * OriginalCRO: Salcedo-Sanz, S., Del Ser, J., Landa-Torres, I., Gil-López, S., & Portilla-Figueras, J. A. (2014). The coral reefs optimization algorithm: a novel metaheuristic for efficiently solving optimization problems. The Scientific World Journal, 2014. * OCRO: 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.

  • COA - Coyote Optimization Algorithm * OriginalCOA: Pierezan, J., & Coelho, L. D. S. (2018, July). Coyote optimization algorithm: a new metaheuristic for global optimization problems. In 2018 IEEE congress on evolutionary computation (CEC) (pp. 1-8). IEEE.

  • CHIO - Coronavirus Herd Immunity Optimization * OriginalCHIO: Al-Betar, M. A., Alyasseri, Z. A. A., Awadallah, M. A., & Abu Doush, I. (2021). Coronavirus herd immunity optimizer (CHIO). Neural Computing and Applications, 33(10), 5011-5042. * BaseCHIO: The developed version

  • CGO - Chaos Game Optimization * OriginalCGO: Talatahari, S., & Azizi, M. (2021). Chaos Game Optimization: a novel metaheuristic algorithm. Artificial Intelligence Review, 54(2), 917-1004.

D.

  • DE - Differential Evolution * BaseDE: 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. * JADE: Zhang, J., & Sanderson, A. C. (2009). JADE: adaptive differential evolution with optional external archive. IEEE Transactions on evolutionary computation, 13(5), 945-958. * SADE: Qin, A. K., & Suganthan, P. N. (2005, September). Self-adaptive differential evolution algorithm for numerical optimization. In 2005 IEEE congress on evolutionary computation (Vol. 2, pp. 1785-1791). IEEE. * SHADE: Tanabe, R., & Fukunaga, A. (2013, June). Success-history based parameter adaptation for differential evolution. In 2013 IEEE congress on evolutionary computation (pp. 71-78). IEEE. * L_SHADE: Tanabe, R., & Fukunaga, A. S. (2014, July). Improving the search performance of SHADE using linear population size reduction. In 2014 IEEE congress on evolutionary computation (CEC) (pp. 1658-1665). IEEE. * SAP_DE: Teo, J. (2006). Exploring dynamic self-adaptive populations in differential evolution. Soft Computing, 10(8), 673-686.

  • DSA - Differential Search Algorithm (not done) * BaseDSA: Civicioglu, P. (2012). Transforming geocentric cartesian coordinates to geodetic coordinates by using differential search algorithm. Computers & Geosciences, 46, 229-247.

  • DO - Dragonfly Optimization * OriginalDO: Mirjalili, S. (2016). Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Computing and Applications, 27(4), 1053-1073.

E.

  • ES - Evolution Strategies . * OriginalES: Schwefel, H. P. (1984). Evolution strategies: A family of non-linear optimization techniques based on imitating some principles of organic evolution. Annals of Operations Research, 1(2), 165-167. * LevyES: Zhang, S., & Salari, E. (2005). Competitive learning vector quantization with evolution strategies for image compression. Optical Engineering, 44(2), 027006.

  • EP - Evolutionary programming . * OriginalEP: Fogel, L. J. (1994). Evolutionary programming in perspective: The top-down view. Computational intelligence: Imitating life. * LevyEP: Lee, C.Y. and Yao, X., 2001, May. Evolutionary algorithms with adaptive lévy mutations. In Proceedings of the 2001 congress on evolutionary computation (IEEE Cat. No. 01TH8546) (Vol. 1, pp. 568-575). IEEE.

  • EHO - Elephant Herding Optimization . * OriginalEHO: Wang, G. G., Deb, S., & Coelho, L. D. S. (2015, December). Elephant herding optimization. In 2015 3rd International Symposium on Computational and Business Intelligence (ISCBI) (pp. 1-5). IEEE.

  • EFO - Electromagnetic Field Optimization . * OriginalEFO: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. * BaseEFO: The developed version

  • EOA - Earthworm Optimisation Algorithm . * OriginalEOA: 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.

  • EO - Equilibrium Optimizer . * OriginalEO: Faramarzi, A., Heidarinejad, M., Stephens, B., & Mirjalili, S. (2019). Equilibrium optimizer: A novel optimization algorithm. Knowledge-Based Systems. * ModifiedEO: Gupta, S., Deep, K., & Mirjalili, S. (2020). An efficient equilibrium optimizer with mutation strategy for numerical optimization. Applied Soft Computing, 96, 106542. * AdaptiveEO: Wunnava, A., Naik, M. K., Panda, R., Jena, B., & Abraham, A. (2020). A novel interdependence based multilevel thresholding technique using adaptive equilibrium optimizer. Engineering Applications of Artificial Intelligence, 94, 103836.

F.

  • FFA - Firefly Algorithm * OriginalFFA: Łukasik, S., & Żak, S. (2009, October). Firefly algorithm for continuous constrained optimization tasks. In International conference on computational collective intelligence (pp. 97-106). Springer, Berlin, Heidelberg.

  • FA - Fireworks algorithm * OriginalFA: Tan, Y., & Zhu, Y. (2010, June). Fireworks algorithm for optimization. In International conference in swarm intelligence (pp. 355-364). Springer, Berlin, Heidelberg.

  • FPA - Flower Pollination Algorithm * OriginalFPA: Yang, X. S. (2012, September). Flower pollination algorithm for global optimization. In International conference on unconventional computing and natural computation (pp. 240-249). Springer, Berlin, Heidelberg.

  • FBIO - Forensic-Based Investigation Optimization * OriginalFBIO: Chou, J.S. and Nguyen, N.M., 2020. FBI inspired meta-optimization. Applied Soft Computing, p.106339. * BaseFBIO: Fathy, A., Rezk, H. and Alanazi, T.M., 2021. Recent approach of forensic-based investigation algorithm for optimizing fractional order PID-based MPPT with proton exchange membrane fuel cell.IEEE Access,9, pp.18974-18992.

  • FOA - Fruit-fly Optimization Algorithm * OriginalFOA: Pan, W. T. (2012). A new fruit fly optimization algorithm: taking the financial distress model as an example. Knowledge-Based Systems, 26, 69-74. * BaseFOA: The developed version * WhaleFOA: Fan, Y., Wang, P., Heidari, A. A., Wang, M., Zhao, X., Chen, H., & Li, C. (2020). Boosted hunting-based fruit fly optimization and advances in real-world problems. Expert Systems with Applications, 159, 113502.

G.

  • GA - Genetic Algorithm * BaseGA: Holland, J. H. (1992). Genetic algorithms. Scientific american, 267(1), 66-73. * SingleGA: De Falco, I., Della Cioppa, A. and Tarantino, E., 2002. Mutation-based genetic algorithm: performance evaluation. Applied Soft Computing, 1(4), pp.285-299. * MultiGA: De Jong, K.A. and Spears, W.M., 1992. A formal analysis of the role of multi-point crossover in genetic algorithms. Annals of mathematics and Artificial intelligence, 5(1), pp.1-26.

  • GWO - Grey Wolf Optimizer * OriginalGWO: Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in engineering software, 69, 46-61. * RW_GWO: Gupta, S., & Deep, K. (2019). A novel random walk grey wolf optimizer. Swarm and evolutionary computation, 44, 101-112.

  • GOA - Grasshopper Optimisation Algorithm * OriginalGOA: Saremi, S., Mirjalili, S., & Lewis, A. (2017). Grasshopper optimisation algorithm: theory and application. Advances in Engineering Software, 105, 30-47.

  • GCO - Germinal Center Optimization * OriginalGCO: 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. * BaseGCO: The developed version

  • GSKA - Gaining Sharing Knowledge-based Algorithm * OriginalGSKA: Mohamed, A. W., Hadi, A. A., & Mohamed, A. K. (2019). Gaining-sharing knowledge based algorithm for solving optimization problems: a novel nature-inspired algorithm. International Journal of Machine Learning and Cybernetics, 1-29. * BaseGSKA: Mohamed, A.W., Hadi, A.A., Mohamed, A.K. and Awad, N.H., 2020, July. Evaluating the performance of adaptive GainingSharing knowledge based algorithm on CEC 2020 benchmark problems. In 2020 IEEE Congress on Evolutionary Computation (CEC) (pp. 1-8). IEEE.

  • GBO - Gradient-Based Optimizer * OriginalGBO: Ahmadianfar, I., Bozorg-Haddad, O., & Chu, X. (2020). Gradient-based optimizer: A new metaheuristic optimization algorithm. Information Sciences, 540, 131-159.

H.

  • HC - Hill Climbing . * OriginalHC: Talbi, E. G., & Muntean, T. (1993, January). Hill-climbing, simulated annealing and genetic algorithms: a comparative study and application to the mapping problem. In [1993] Proceedings of the Twenty-sixth Hawaii International Conference on System Sciences (Vol. 2, pp. 565-573). IEEE. * SwarmHC: The developed version based on swarm-based idea (Original is single-solution based method)

  • HS - Harmony Search . * OriginalHS: Geem, Z. W., Kim, J. H., & Loganathan, G. V. (2001). A new heuristic optimization algorithm:harmony search. simulation, 76(2), 60-68. * BaseHS: The developed version

  • HHO - Harris Hawks Optimization . * OriginalHHO: 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 . * OriginalHGSO: 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.

  • HGS - Hunger Games Search . * OriginalHGS: Yang, Y., Chen, H., Heidari, A. A., & Gandomi, A. H. (2021). Hunger games search:Visions, conception, implementation, deep analysis, perspectives, and towards performance shifts. Expert Systems with Applications, 177, 114864.

  • HHOA - Horse Herd Optimization Algorithm (not done) . * BaseHHOA: MiarNaeimi, F., Azizyan, G., & Rashki, M. (2021). Horse herd optimization algorithm: A nature-inspired algorithm for high-dimensional optimization problems. Knowledge-Based Systems, 213, 106711.

I.

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

  • ICA - Imperialist Competitive Algorithm * OriginalICA: Atashpaz-Gargari, E., & Lucas, C. (2007, September). Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In 2007 IEEE congress on evolutionary computation (pp. 4661-4667). Ieee.

J.

  • JA - Jaya Algorithm * OriginalJA: Rao, R. (2016). Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems. International Journal of Industrial Engineering Computations, 7(1), 19-34. * BaseJA: The developed version * LevyJA: Iacca, G., dos Santos Junior, V. C., & de Melo, V. V. (2021). An improved Jaya optimization algorithm with Levy flight. Expert Systems with Applications, 165, 113902.

K.

L.

  • LCO - Life Choice-based Optimization * OriginalLCO: Khatri, A., Gaba, A., Rana, K. P. S., & Kumar, V. (2019). A novel life choice-based optimizer. Soft Computing, 1-21. * BaseLCO: The developed version * ImprovedLCO: The improved version using Gaussian distribution and Mutation Mechanism

M.

  • MA - Memetic Algorithm * OriginalMA: Moscato, P. (1989). On evolution, search, optimization, genetic algorithms and martial arts: Towards memetic algorithms. Caltech concurrent computation program, C3P Report, 826, 1989.

  • MFO - Moth Flame Optimization * OriginalMFO: Mirjalili, S. (2015). Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-based systems, 89, 228-249. * BaseMFO: The developed version

  • MVO - Multi-Verse Optimizer * OriginalMVO: 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. * BaseMVO: The developed version

  • MSA - Moth Search Algorithm * OriginalMSA: Wang, G. G. (2018). Moth search algorithm: a bio-inspired metaheuristic algorithm for global optimization problems. Memetic Computing, 10(2), 151-164.

  • MRFO - Manta Ray Foraging Optimization * OriginalMRFO: 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.

N.

  • NRO - Nuclear Reaction Optimization * OriginalNRO: 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.

  • NMRA - Nake Mole-Rat Algorithm * OriginalNMRA: Salgotra, R., & Singh, U. (2019). The naked mole-rat algorithm. Neural Computing and Applications, 31(12), 8837-8857. * ImprovedNMRA: Singh, P., Mittal, N., Singh, U. and Salgotra, R., 2021. Naked mole-rat algorithm with improved exploration and exploitation capabilities to determine 2D and 3D coordinates of sensor nodes in WSNs. Arabian Journal for Science and Engineering, 46(2), pp.1155-1178.

O.

P.

  • PSO - Particle Swarm Optimization * OriginalPSO: 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. * PPSO: Ghasemi, M., Akbari, E., Rahimnejad, A., Razavi, S. E., Ghavidel, S., & Li, L. (2019). Phasor particle swarm optimization: a simple and efficient variant of PSO. Soft Computing, 23(19), 9701-9718. * HPSO_TVAC: Ghasemi, M., Aghaei, J., & Hadipour, M. (2017). New self-organising hierarchical PSO with jumping time-varying acceleration coefficients. Electronics Letters, 53(20), 1360-1362. * C_PSO: Liu, B., Wang, L., Jin, Y. H., Tang, F., & Huang, D. X. (2005). Improved particle swarm optimization combined with chaos. Chaos, Solitons & Fractals, 25(5), 1261-1271. * CL_PSO: Liang, J. J., Qin, A. K., Suganthan, P. N., & Baskar, S. (2006). Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE transactions on evolutionary computation, 10(3), 281-295.

  • PFA - Pathfinder Algorithm * OriginalPFA: Yapici, H., & Cetinkaya, N. (2019). A new meta-heuristic optimizer: Pathfinder algorithm. Applied Soft Computing, 78, 545-568.

  • PSS - Pareto-like Sequential Sampling * OriginalPSS: Shaqfa, M., & Beyer, K. (2021). Pareto-like sequential sampling heuristic for global optimisation. Soft Computing, 25(14), 9077-9096.

Q.

  • QSA - Queuing Search Algorithm * OriginalQSA: 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. * BaseQSA: The developed version * OppoQSA: Zheng, X. and Nguyen, H., 2022. A novel artificial intelligent model for predicting water treatment efficiency of various biochar systems based on artificial neural network and queuing search algorithm. Chemosphere, 287, p.132251. * LevyQSA: Abderazek, H., Hamza, F., Yildiz, A.R., Gao, L. and Sait, S.M., 2021. A comparative analysis of the queuing search algorithm, the sine-cosine algorithm, the ant lion algorithm to determine the optimal weight design problem of a spur gear drive system. Materials Testing, 63(5), pp.442-447. * ImprovedQSA: Nguyen, B.M., Hoang, B., Nguyen, T. and Nguyen, G., 2021. nQSV-Net: a novel queuing search variant for global space search and workload modeling. Journal of Ambient Intelligence and Humanized Computing, 12(1), pp.27-46.

R.

S.

  • SA - Simulated Annealling * OriginalSA: Kirkpatrick, S., Gelatt Jr, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. science, 220(4598), 671-680. * GaussianSA: Van Laarhoven, P. J., Aarts, E. H., van Laarhoven, P. J., & Aarts, E. H. (1987). Simulated annealing (pp. 7-15). Springer Netherlands. * SwarmSA: My developed version

  • SSpiderO - Social Spider Optimization * OriginalSSpiderO: 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.

  • SSpiderA - Social Spider Algorithm * OriginalSSpiderA: James, J. Q., & Li, V. O. (2015). A social spider algorithm for global optimization. Applied Soft Computing, 30, 614-627.

  • SCA - Sine Cosine Algorithm * OriginalSCA: Mirjalili, S. (2016). SCA: a sine cosine algorithm for solving optimization problems. Knowledge-Based Systems, 96, 120-133. * BaseSCA: Attia, A.F., El Sehiemy, R.A. and Hasanien, H.M., 2018. Optimal power flow solution in power systems using a novel Sine-Cosine algorithm. International Journal of Electrical Power & Energy Systems, 99, pp.331-343.

  • SRSR - Swarm Robotics Search And Rescue * OriginalSRSR: 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.

  • SBO - Satin Bowerbird Optimizer * OriginalSBO: 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. * BaseSBO: The developed version

  • SHO - Spotted Hyena Optimizer * OriginalSHO: Dhiman, G., & Kumar, V. (2017). Spotted hyena optimizer: a novel bio-inspired based metaheuristic technique for engineering applications. Advances in Engineering Software, 114, 48-70.

  • SSO - Salp Swarm Optimization * OriginalSSO: Mirjalili, S., Gandomi, A. H., Mirjalili, S. Z., Saremi, S., Faris, H., & Mirjalili, S. M. (2017). Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems. Advances in Engineering Software, 114, 163-191.

  • SFO - Sailfish Optimizer * OriginalSFO: 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. * ImprovedSFO: Li, L.L., Shen, Q., Tseng, M.L. and Luo, S., 2021. Power system hybrid dynamic economic emission dispatch with wind energy based on improved sailfish algorithm. Journal of Cleaner Production, 316, p.128318.

  • SARO - Search And Rescue Optimization * OriginalSARO: 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. * BaseSARO: The developed version using Levy-flight

  • SSDO - Social Ski-Driver Optimization * OriginalSSDO: 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.

  • SLO - Sea Lion Optimization * OriginalSLO: Masadeh, R., Mahafzah, B. A., & Sharieh, A. (2019). Sea Lion Optimization Algorithm. Sea, 10(5). * ImprovedSLO: The developed version * ModifiedSLO: Masadeh, R., Alsharman, N., Sharieh, A., Mahafzah, B.A. and Abdulrahman, A., 2021. Task scheduling on cloud computing based on sea lion optimization algorithm. International Journal of Web Information Systems.

  • SMA - Slime Mould Algorithm * OriginalSMA: Li, S., Chen, H., Wang, M., Heidari, A. A., & Mirjalili, S. (2020). Slime mould algorithm: A new method for stochastic optimization. Future Generation Computer Systems. * BaseSMA: The developed version

  • SSA - Sparrow Search Algorithm * OriginalSSA: Jiankai Xue & Bo Shen (2020) A novel swarm intelligence optimization approach: sparrow search algorithm, Systems Science & Control Engineering, 8:1, 22-34, DOI: 10.1080/21642583.2019.1708830 * BaseSSA: The developed version

T.

  • TLO - Teaching Learning Optimization * OriginalTLO: 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. * BaseTLO: Rao, R., & Patel, V. (2012). An elitist teaching-learning-based optimization algorithm for solving complex constrained optimization problems. International Journal of Industrial Engineering Computations, 3(4), 535-560. * ImprovedTLO: Rao, R. V., & Patel, V. (2013). An improved teaching-learning-based optimization algorithm for solving unconstrained optimization problems. Scientia Iranica, 20(3), 710-720.

  • TWO - Tug of War Optimization * OriginalTWO: Kaveh, A., & Zolghadr, A. (2016). A novel meta-heuristic algorithm: tug of war optimization. Iran University of Science & Technology, 6(4), 469-492. * OppoTWO: Kaveh, A., Almasi, P. and Khodagholi, A., 2022. Optimum Design of Castellated Beams Using Four Recently Developed Meta-heuristic Algorithms. Iranian Journal of Science and Technology, Transactions of Civil Engineering, pp.1-13. * LevyTWO: The developed version using Levy-flight * ImprovedTWO: Nguyen, T., Hoang, B., Nguyen, G., & Nguyen, B. M. (2020). A new workload prediction model using extreme learning machine and enhanced tug of war optimization. Procedia Computer Science, 170, 362-369.

U.

V.

  • VCS - Virus Colony Search * OriginalVCS: Li, M. D., Zhao, H., Weng, X. W., & Han, T. (2016). A novel nature-inspired algorithm for optimization: Virus colony search. Advances in Engineering Software, 92, 65-88. * BaseVCS: The developed version

W.

  • WCA - Water Cycle Algorithm * OriginalWCA: Eskandar, H., Sadollah, A., Bahreininejad, A., & Hamdi, M. (2012). Water cycle algorithm–A novel metaheuristic optimization method for solving constrained engineering optimization problems. Computers & Structures, 110, 151-166.

  • WOA - Whale Optimization Algorithm * OriginalWOA: Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in engineering software, 95, 51-67. * HI_WOA: Tang, C., Sun, W., Wu, W., & Xue, M. (2019, July). A hybrid improved whale optimization algorithm. In 2019 IEEE 15th International Conference on Control and Automation (ICCA) (pp. 362-367). IEEE.

  • WHO - Wildebeest Herd Optimization * OriginalWHO: 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 * OriginalWDO: Bayraktar, Z., Komurcu, M., Bossard, J.A. and Werner, D.H., 2013. The wind driven optimization technique and its application in electromagnetics. IEEE transactions on antennas and propagation, 61(5), pp.2745-2757.

X.

Y.

Z.

:maxdepth: 4

License

The project is licensed under GNU General Public License (GPL) V3 license.