Citations

If you use mealpy in your project, I would appreciate citations:

@software{thieu_nguyen_2020_3711949,
   author       = {Nguyen Van Thieu},
   title        = {A collection of the state-of-the-art Meta-heuristic 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.

If you have an open-ended or a research question, you can contact me via Email or Research Gate

Classification Table

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

    • Evolutionary-based: Idea from Darwin’s law of natural selection, evolutionary computing

    • Swarm-based: Idea from movement, interaction of birds, organization of social …

    • Physics-based: Idea from physics law such as Newton’s law of universal gravitation, black hole, multiverse

    • Human-based: Idea from human interaction such as queuing search, teaching learning, …

    • Biology-based: Idea from biology creature (or microorganism),…

    • System-based: Idea from eco-system, immune-system, network-system, …

    • Math-based: Idea from mathematical form or mathematical law such as sin-cosin

    • Music-based: Idea from music instrument

    • Probabilistic-base: Probabilistic based algorithm

    • Dummy: Non-sense algorithms and Non-sense papers (code proofs)

  • DBSP: Difference Between Sequential and Parallel training mode, the results of some algorithms may various due to the training mode.

    • significant: The results will be very different (because the selecting process - select a previous or the next solution to update current solution)

    • in-significant: The results will not much different (because the selecting process - select a random solution in population to update the current solution)

  • Performance (Personal Opinion):

    • good: working good with benchmark functions (convergence good)

    • not good: not working good with benchmark functions (convergence not good, not balance the exploration and exploitation phase)

  • 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 “good” performance and have only 2 paras meaning the algorithms are outstanding

  • 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: 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 which difficulty is “easy” or “medium” difficulty level.

Group

STT

Name

Short

Year

DBSP

Performance

Paras

Difficulty

Evolutionary

1

Evolutionary Programming

EP

1964

no

not good

3

easy

Evolutionary

2

Evolution Strategies

ES

1971

no

not good

3

easy

Evolutionary

3

Memetic Algorithm

MA

1989

significant

not good

7

easy

Evolutionary

3

Genetic Algorithm

GA

1992

in-significant

good

4

easy

Evolutionary

4

Differential Evolution

DE

1997

in-significant

good

4

easy

Evolutionary

5

Flower Pollination Algorithm

FPA

2014

in-significant

good

3

easy

Evolutionary

6

Coral Reefs Optimization

CRO

2014

in-significant

good

7

medium

0

7

Swarm

1

Particle Swarm Optimization

PSO

1995

in-significant

good

6

easy

Swarm

2

Bacterial Foraging Optimization

BFO

2002

no

good

9

hard

Swarm

3

Bees Algorithm

BeesA

2005

no

not good

9

medium

Swarm

4

Cat Swarm Optimization

CSO

2006

significant

not good

9

hard

Swarm

5

Ant Colony Optimization

ACO

2006

in-significant

good

5

medium

Swarm

6

Artificial Bee Colony

ABC

2007

no

good

8

easy

Swarm

7

Ant Colony Optimization

ACO-R

2008

in-significant

good

5

medium

Swarm

8

Cuckoo Search Algorithm

CSA

2009

in-significant

good

3

easy

Swarm

9

Firefly Algorithm

FFA

2009

significant

good

8

medium

Swarm

10

Fireworks Algorithm

FA

2010

significant

good

7

medium

Swarm

11

Bat Algorithm

BA

2010

no

not good

5

easy

Swarm

12

Fruit-fly Optimization Algorithm

FOA

2012

no

not good

2

easy

Swarm

13

Social Spider Optimization

SSpiderO

2013

no

not good

3

hard*

Swarm

14

Grey Wolf Optimizer

GWO

2014

no

good

2

easy

Swarm

15

Social Spider Algorithm

SSpiderA

2015

no

not good

5

easy

Swarm

16

Ant Lion Optimizer

ALO

2015

no

good

2

medium

Swarm

17

Moth Flame Optimization

MFO

2015

no

good

2

easy

Swarm

18

Elephant Herding Optimization

EHO

2015

significant

good

5

easy

Swarm

19

Jaya Algorithm

JA

2016

no

good

2

easy

Swarm

20

Whale Optimization Algorithm

WOA

2016

no

good

2

easy

Swarm

21

Dragonfly Optimization

DO

2016

significant

good

2

medium

Swarm

22

Bird Swarm Algorithm

BSA

2016

in-significant

good

9

medium

Swarm

23

Spotted Hyena Optimizer

SHO

2017

no

good

6

medium

Swarm

24

Salp Swarm Optimization

SSO

2017

significant

good

2

easy

Swarm

25

Swarm Robotics Search And Rescue

SRSR

2017

in-significant

good

2

hard*

Swarm

26

Grasshopper Optimisation Algorithm

GOA

2017

no

not good

3

easy

Swarm

27

Coyote Optimization Algorithm

COA

2018

no

good

3

medium

Swarm

28

Moth Search Algorithm

MSA

2018

no

good

5

easy

Swarm

29

Sea Lion Optimization

SLO

2019

no

good

2

medium

Swarm

30

Nake Mole-Rat Algorithm

NMRA

2019

in-significant

good

3

easy

Swarm

31

Bald Eagle Search

BES

2019

in-significant

good

7

medium

Swarm

32

Pathfinder Algorithm

PFA

2019

significant

good

2

easy

Swarm

33

Sailfish Optimizer

SFO

2019

no

good

5

medium

Swarm

34

Harris Hawks Optimization

HHO

2019

significant

good

2

medium

Swarm

35

Manta Ray Foraging Optimization

MRFO

2020

no

good

3

easy

Swarm

36

Sparrow Search Algorithm

SSA

2020

no

good

5

medium

Swarm

37

Hunger Games Search

HGS

2021

no

good

4

medium

Swarm

38

Aquila Optimizer

AO

2021

no

good

2

easy

0

39

Physics

1

Simulated Annealling

SA

1987

in-significant

not good

9

medium

Physics

2

Wind Driven Optimization

WDO

2013

in-significant

good

7

easy

Physics

3

Multi-Verse Optimizer

MVO

2016

in-significant

good

3

easy

Physics

4

Tug of War Optimization

TWO

2016

in-significant

not good

2

easy

Physics

5

Electromagnetic Field Optimization

EFO

2016

significant

good

6

easy

Physics

6

Nuclear Reaction Optimization

NRO

2019

in-significant

good

2

hard*

Physics

7

Henry Gas Solubility Optimization

HGSO

2019

significant

good

3

medium

Physics

8

Atom Search Optimization

ASO

2019

no

good

4

medium

Physics

9

Equilibrium Optimizer

EO

2019

no

good

2

easy

Physics

10

Archimedes Optimization Algorithm

ArchOA

2021

in-significant

good

6

medium

0

11

Human

1

Culture Algorithm

CA

1994

no

not good

3

easy

Human

2

Imperialist Competitive Algorithm

ICA

2007

significant

good

10

hard*

Human

3

Teaching Learning-based Optimization

TLO

2011

in-significant

good

2

easy

Human

4

Brain Storm Optimization

BSO

2011

in-significant

not good

10

medium

Human

5

Queuing Search Algorithm

QSA

2019

in-significant

good

2

hard

Human

6

Search And Rescue Optimization

SARO

2019

in-significant

good

4

medium

Human

7

Life Choice-Based Optimization

LCO

2019

significant

good

2

easy

Human

8

Social Ski-Driver Optimization

SSDO

2019

significant

good

2

easy

Human

9

Gaining Sharing Knowledge-based Algorithm

GSKA

2019

significant

good

6

easy

Human

10

Coronavirus Herd Immunity Optimization

CHIO

2020

significant

not good

4

medium

Human

11

Forensic-Based Investigation Optimization

FBIO

2020

no

good

2

medium

Human

12

Battle Royale Optimization

BRO

2020

in-significant

not good

2

medium

0

13

Bio

1

Invasive Weed Optimization

IWO

2006

no

good

5

easy

Bio

2

Biogeography-Based Optimization

BBO

2008

in-significant

good

4

easy

Bio

3

Virus Colony Search

VCS

2016

significant

good

4

hard*

Bio

4

Satin Bowerbird Optimizer

SBO

2017

in-significant

good

5

easy

Bio

5

Earthworm Optimisation Algorithm

EOA

2018

in-significant

good

8

medium

Bio

6

Wildebeest Herd Optimization

WHO

2019

no

good

12

medium

Bio

7

Slime Mould Algorithm

SMA

2020

in-significant

good

3

easy

0

8

System

1

Germinal Center Optimization

GCO

2018

in-significant

good

4

medium

System

2

Water Cycle Algorithm

WCA

2012

in-significant

good

5

medium

System

3

Artificial Ecosystem-based Optimization

AEO

2019

no

good

2

easy

0

4

Math

1

Hill Climbing

HC

1993

no

not good

3

easy

Math

2

Sine Cosine Algorithm

SCA

2016

no

good

2

easy

Math

3

Gradient-Based Optimizer

GBO

2020

no

good

3

medium

Math

4

Arithmetic Optimization Algorithm

AOA

2021

no

good

6

easy

Math

5

Chaos Game Optimization

CGO

2021

no

good

2

easy

0

6

Music

1

Harmony Search

HS

2001

no

good

5

easy

0

2

Probabilistic

1

Cross-Entropy Method

CEM

1997

in-significant

good

4

easy

0

2

Dummy

1

Pigeon-Inspired Optimization

PIO

2014

good

2

medium

Dummy

2

Artificial Algae Algorithm

AAA

2015

not good

5

medium

Dummy

3

Rhino Herd Optimization

RHO

2018

not good

6

easy

Dummy

4

Emperor Penguin Optimizer

EPO

2018

good

2

easy

Dummy

5

Butterfly Optimization Algorithm

BOA

2019

not good

6

medium

Dummy

6

Blue Monkey Optimization

BMO

2019

not good

3

medium

Dummy

7

Sandpiper Optimization Algorithm

SOA

2020

not good

2

easy

Dummy

8

Black Widow Optimization

BWO

2020

good

5

medium

Model References

A.

ABC - Artificial Bee Colony

  • BaseABC: 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

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

ALO - Ant Lion Optimizer

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.

  • AdaptiveAEO: My adaptive version

  • 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

  • BaseASO: 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.

  • BaseBFO: Yan, X., Zhu, Y., Zhang, H., Chen, H., & Niu, B. (2012). An adaptive bacterial foraging optimization algorithm with lifecycle and social learning. Discrete Dynamics in Nature and Society, 2012.

BeesA - Bees Algorithm

  • BaseBeesA: 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: My version

BA - Bat Algorithm

  • BasicBA: 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.

  • OriginalBA: The original version

  • BaseBA: My modified version

BSO - Brain Storm Optimization

  • BaseBSO: . Shi, Y. (2011, June). Brain storm optimization algorithm. In International conference in swarm intelligence (pp. 303-309). Springer, Berlin, Heidelberg.

  • ImprovedBSO: My improved version using levy-flight

BSA - Bird Swarm Algorithm

  • BaseBSA: 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

  • BaseBES: 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: My modified 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

  • BaseCEM: 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

  • BaseCSO: 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

  • BaseCSA: 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

  • BaseCRO: 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

  • BaseCOA: 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: My changed 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

  • BaseDO: 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 .

  • BaseES: 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: My modified version using Levy-flight

EP - Evolutionary programming .

  • BaseEP: Fogel, L. J. (1994). Evolutionary programming in perspective: The top-down view. Computational intelligence: Imitating life.

  • LevyEP: My modified version using Levy-flight

EHO - Elephant Herding Optimization .

  • BaseEHO: 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: My modified version using Levy-flight

EOA - Earthworm Optimisation Algorithm .

  • BaseEOA: 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 .

  • BaseEO: 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.

FireflyA - Firefly Algorithm

  • BaseFireflyA: 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

  • BaseFA: 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

  • BaseFPA: 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: My version

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: My version

  • WFOA: 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.

GWO - Grey Wolf Optimizer

  • BaseGWO: 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

  • BaseGOA: 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: My modified 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: My modified version

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.

  • BaseHC My modified version

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: My modified version

HHO - Harris Hawks Optimization .

  • BaseHHO: 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 .

  • BaseHGSO: 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

  • BaseICA: 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: My 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: My version

  • ImprovedLCO: My improved version using Gaussian distribution and Mutation Mechanism

M.

MA - Memetic Algorithm

  • BaseMA: 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: My 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: My modified version

MSA - Moth Search Algorithm

  • BaseMSA: 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

  • BaseMRFO: 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

  • BaseNRO: 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.

NMR - Nake Mole-Rat Algorithm

  • BaseNMR: Salgotra, R., & Singh, U. (2019). The naked mole-rat algorithm. Neural Computing and Applications, 31(12), 8837-8857.

  • ImprovedNMR: My version using mutation probability, levy-flight and crossover operator

O.

P.

PSO - Particle Swarm Optimization

  • BasePSO: 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

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

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: My version

  • OppoQSA: My version using opposition-based learning

  • LevyQSA: My version using Levy-flight

  • ImprovedQSA: My version using Levy-flight and Opposition-based learning

R.

S.

SA - Simulated Annealling

  • BaseSA: . Van Laarhoven, P. J., & Aarts, E. H. (1987). Simulated annealing. In Simulated annealing: Theory and applications (pp. 7-15). Springer, Dordrecht.

SSpiderO - Social Spider Optimization

  • BaseSSpiderO: 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

  • BaseSSpiderA: 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: My modified version

SRSR - Swarm Robotics Search And Rescue

  • BaseSRSR: 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: My modified version

SSO - Salp Swarm Optimization

  • BaseSSO: 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

  • BaseSFO: 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: My improved version

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: My modified version using Levy-flight

SSDO - Social Ski-Driver Optimization

  • BaseSSDO: 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

  • BaseSLO: Masadeh, R., Mahafzah, B. A., & Sharieh, A. (2019). Sea Lion Optimization Algorithm. Sea, 10(5).

  • ISLO: My improved version

  • ModifiedSLO: My modifed version using Levy-flight

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: My modified 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: My modified 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.

  • ITLO: 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

  • BaseTWO: Kaveh, A., & Zolghadr, A. (2016). A novel meta-heuristic algorithm: tug of war optimization. Iran University of Science & Technology, 6(4), 469-492.

  • OppoTWO: 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.

  • LevyTWO: My version using Levy-flight

  • ImprovedTWO: My version using both Levy-flight and opposition-based learning

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: My modified version

W.

WCA - Water Cycle Algorithm

  • BaseWCA: 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

  • BaseWOA: 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

  • BaseWHO: 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

  • BaseWDO: 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.

X.

Y.

Z.

Dummy Algorithms

AAA - Artificial Algae Algorithm .

  • OriginalAAA: Uymaz, S. A., Tezel, G., & Yel, E. (2015). Artificial algae algorithm (AAA) for nonlinear global optimization. Applied Soft Computing, 31, 153-171.

  • BaseAAA: My trial version

BWO - Black Widow Optimization .

  • OriginalBWO: 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.

  • BaseBWO: My trial version

BOA - Butterfly Optimization Algorithm.

  • OriginalBOA: Arora, S., & Singh, S. (2019). Butterfly optimization algorithm: a novel approach for global optimization. Soft Computing, 23(3), 715-734.

  • BaseBOA: My trial version

  • AdaptiveBOA: Singh, B., & Anand, P. (2018). A novel adaptive butterfly optimization algorithm. International Journal of Computational Materials Science and Engineering, 7(04), 1850026.

BMO - Blue Monkey Optimization .

  • OriginalBMO: Blue Monkey Optimization: (2019) The Blue Monkey: A New Nature Inspired Metaheuristic Optimization Algorithm. DOI: http://dx.doi.org/10.21533/pen.v7i3.621

  • BaseBMO: My trial version

EPO - Emperor Penguin Optimizer .

  • OriginalEPO: Dhiman, G., & Kumar, V. (2018). Emperor penguin optimizer: A bio-inspired algorithm for engineering problems. Knowledge-Based Systems, 159, 20-50.

  • BaseEPO: My trial version

PIO - Pigeon-Inspired Optimization .

  • None: Duan, H., & Qiao, P. (2014). Pigeon-inspired optimization: a new swarm intelligence optimizer for air robot path planning. International journal of intelligent computing and cybernetics.

  • BasePIO: My trial version, since the Original version not working.

  • LevyPIO: My trial version using Levy-flight

RHO - Rhino Herd Optimization .

  • OriginalRHO: 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.

  • BaseRHO: My developed version

  • LevyRHO: My developed using Levy-flight

SOA - Sandpiper Optimization Algorithm .

  • OriginalSOA: Kaur, A., Jain, S., & Goel, S. (2020). Sandpiper optimization algorithm: a novel approach for solving real-life engineering problems. Applied Intelligence, 50(2), 582-619.

  • BaseSOA: My trial version

STOA - Sooty Tern Optimization Algorithm.

  • BaseSTOA: Sooty Tern Optimization Algorithm: Dhiman, G., & Kaur, A. (2019). STOA: A bio-inspired based optimization algorithm for industrial engineering problems. Engineering Applications of Artificial Intelligence, 82, 148-174.

RRO - Raven Roosting Optimizaiton.

  • OriginalRRO: Brabazon, A., Cui, W., & O’Neill, M. (2016). The raven roosting optimisation algorithm. Soft Computing, 20(2), 525-545.

  • IRRO: Torabi, S., & Safi-Esfahani, F. (2018). Improved raven roosting optimization algorithm (IRRO). Swarm and Evolutionary Computation, 40, 144-154.

  • BaseRRO: My developed version

License

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