الگوریتم های بهینه سازی فراابتکاری یا فرامکاشفه ای را میتوان بر اساس منبع الهام آنها در حالت کلی در 10 دسته ، تقسیم بندی کرد که عبارتند از :

  1. الگوریتم تکاملی: الگوریتم های مبتنی بر تکامل
  2. الگوریتم توده یا swarm : الگوریتم های مبتنی بر Swarm
  3. فیزیک: الگوریتم های مبتنی بر فیزیک
  4. انسان: الگوریتم های انسان محور
  5. بیو یا زیست : الگوریتم های مبتنی بر زیست شناسی
  6. سیستم : الگوریتم های مبتنی بر سیستم (سیستم زیست محیطی ، سیستم ایمنی بدن ، سیستم شبکه ، …)
  7. ریاضیات : الگوریتم های مبتنی بر ریاضیات
  8. موسیقی : الگوریتم های مبتنی بر موسیقی
  9. احتمال: الگوریتم مبتنی بر احتمال
  10. ساختگی: الگوریتم های غیر حس و مقالات غیر حس (اثبات کد)
    • بعضی از مقالات اصلی بسیار نامشخص هستند (پارامترها ، معادلات ، جریان الگوریتم) زیرا من آن را به مقالات ساختگی و الگوریتم های ساختگی طبقه بندی می کنم .

در ادامه الگوریتم های فراابتکاری قرار گرفته در هر دسته را لیست کرده ایم:

در جدول زیر ما از پارامترهایی استفاده کرده ایم که ابتدا به توضیح آنها می پردازم:

1- لوی یا Levy : آیا در الگوریتم از از تکنیک پواز levy استفاده شده است یا خیر.

2- قدرت الگوریتم :

  • ضعیف: با عملکردهای یکپارچه و چند حالته خوب کار می کند
  • قوی: با عملکردهای تک حالته ، چند حالته ، برخی ترکیبی و برخی از ترکیبات خوب کار می کند
  • بهترین: تقریباً با همه نوع عملکرد به خوبی کار می کند

3- تعداد پارامتر ها: تقریباً الگوریتم ها دارای 2 پاراگراف (تعداد تکرار و اندازه جمعیت) هستند و بعلاوه برخی پارامترهای که به هر الگوریتم بستگی دارد.
4- دشواری یا میزان سختی : (نظر شخصی): مشاهده عینی نویسنده. به تعداد پارامترها ، تعداد معادلات ، ایده های اصلی ، زمان صرف کدگذاری ، تعداد خطوط کد

  • آسان: چند پارامتر، چند معادله ، میزان کد بسیار کوتاه
  • متوسط: معادلات بیشتری نسبت به سطح آسان ، کد طولانی تر از سطح آسان است
  • سخت: تعداد زیاد معادلات ، کد طولانی تر از سطح متوسط ​​، مقاله سخت خوانده شده.
  • سخت * – بسیار سخت: تعداد زیاد معادلات ، طول کد خیلی طولانی است ، خواندن مقاله بسیار سخت است.

** برای تازه کار ، توصیه می کنم مقاله الگوریتم های مربوط به نوع دشواری “بهترین یا قوی” ، “آسان یا متوسط” را بخوانید.

لیست الگوریتم های بهینه سازی بر اساس نوع دسته بندی

دسته بندیشمارهنام الگوریتمنام مخففسال انتشارLevyقدرت الگوریتممقیاس بالاتعداد پارامترهامیزان سختی
Evolutionary یا تکاملی1Evolutionary ProgrammingEP1964خیرضعیفخیر3ساده
2Evolution StrategiesES1971خیرضعیفخیر3ساده
3Memetic AlgorithmMA1989خیرضعیفخیر7ساده
3Genetic AlgorithmGA1992خیرقویخیر4ساده
4Differential EvolutionDE1997خیرقویخیر4ساده
5Flower Pollination AlgorithmFPA2014بلهقویخیر3ساده
6Coral Reefs OptimizationCRO2014خیرقویخیر7متوسط
7
Swarm یا مبتنی بر توده1Particle Swarm OptimizationPSO1995خیرقویبله6ساده
2Bacterial Foraging OptimizationBFO2002خیرضعیفخیر11سخت
3Bees AlgorithmBeesA2005خیرضعیفخیر9متوسط
4Cat Swarm OptimizationCSO2006خیرضعیفخیر9سخت
5Ant Colony OptimizationACO2006خیرقویخیر5متوسط
6Artificial Bee ColonyABC2007خیرقویخیر8ساده
7Ant Colony OptimizationACO-R2008خیرقویخیر5متوسط
8Cuckoo Search AlgorithmCSA2009بلهقویبله3ساده
9Firefly AlgorithmFireflyA2009خیرقویخیر8متوسط
10Fireworks AlgorithmFA2010خیرقویخیر7متوسط
11Bat AlgorithmBA2010خیرضعیفخیر5ساده
12Fruit-fly Optimization AlgorithmFOA2012خیرضعیفخیر2ساده
13Social Spider OptimizationSSO2013خیرضعیفخیر3سخت*
14Grey Wolf OptimizerGWO2014خیربهترینبله2ساده
15Social Spider AlgorithmSSA2015خیرضعیفخیر5ساده
16Ant Lion OptimizerALO2015خیرقویبله2متوسط
17Moth Flame OptimizationMFO2015خیرقویخیر2ساده
18Elephant Herding OptimizationEHO2015خیربهترینبله5ساده
19Jaya AlgorithmJA2016خیرقویبله2ساده
20Whale Optimization AlgorithmWOA2016خیربهترینبله2ساده
21Dragonfly OptimizationDO2016خیرقویخیر2متوسط
22Bird Swarm AlgorithmBSA2016خیربهترینبله9متوسط
23Spotted Hyena OptimizerSHO2017خیرضعیفخیر6متوسط
24Salp Swarm OptimizationSalpSO2017خیرقویخیر2ساده
25Swarm Robotics Search And RescueSRSR2017خیربهترینبله2سخت*
26Grasshopper Optimisation AlgorithmGOA2017خیرضعیفخیر3ساده
27Moth Search AlgorithmMSA2018بلهقویخیر5ساده
28Nake Mole-rat AlgorithmNMRA2019خیرقویبله3ساده
29Bald Eagle SearchBES2019خیرقویخیر7متوسط
30Pathfinder AlgorithmPFA2019خیربهترینبله2ساده
31Sailfish OptimizerSFO2019خیربهترینبله5متوسط
32Harris Hawks OptimizationHHO2019بلهبهترینبله2متوسط
33Manta Ray Foraging OptimizationMRFO2020خیربهترینبله3ساده
34Sparrow Search AlgorithmSpaSA2020خیربهترینبله5متوسط
35Hunger Games SearchHGS2021خیربهترینبله4متوسط
36
Physics مبتنیبر فیزیک1Simulated AnneallingSA1987خیرضعیفخیر9متوسط
2Wind Driven OptimizationWDO2013خیرقویبله7ساده
3Multi-Verse OptimizerMVO2016خیرضعیفخیر3ساده
4Tug of War OptimizationTWO2016خیرقویخیر2ساده
5Electromagnetic Field OptimizationEFO2016خیرقویبله6ساده
6Nuclear Reaction OptimizationNRO2019بلهبهترینبله2سخت*
7Henry Gas Solubility OptimizationHGSO2019خیربهترینبله3متوسط
8Atom Search OptimizationASO2019خیرقویخیر4متوسط
9Equilibrium OptimizerEO2019خیربهترینبله2ساده
10
Human یا مبتنی بر انسان1Culture AlgorithmCA1994خیرقویخیر3ساده
2Imperialist Competitive AlgorithmICA2007خیرقویبله10سخت*
3Teaching Learning OptimizationTLO2011خیربهترینبله2ساده
4Brain Storm OptimizationBSO2011خیرضعیفخیر10متوسط
5Queuing Search AlgorithmQSA2019خیرقویبله2سخت
6Search And Rescue OptimizationSARO2019خیرقویبله4متوسط
7Life Choice-Based OptimizationLCBO2019خیرقویبله2ساده
8Social Ski-Driver OptimizationSSDO2019خیربهترینبله2ساده
9Gaining Sharing Kخیرwledge-based AlgorithmGSKA2019خیرقویخیر6ساده
10Coronavirus Herd Immunity OptimizationCHIO2020خیرضعیفخیر4متوسط
11Forensic-Based Investigation OptimizationFBIO2020خیربهترینبله2متوسط
12Battle Royale OptimizationBRO2020خیرضعیفخیر2متوسط
13
Bio یا مبتنی بر زیست1Invasive Weed OptimizationIWO2006خیرقویبله5ساده
2Biogeography-Based OptimizationBBO2008خیرقویبله4ساده
3Virus Colony SearchVCS2016خیربهترینخیر4سخت*
4Satin Bowerbird OptimizerSBO2017خیرقویبله5ساده
5Earthworm Optimisation AlgorithmEOA2018خیرقویبله8متوسط
6Wildebeest Herd OptimizationWHO2019خیرقویبله12متوسط
7Slime Mould AlgorithmSMA2020خیرقویبله3ساده
8
System یا مبتنی بر سیستم1Germinal Center OptimizationGCO2018خیرقویبله4متوسط
2Water Cycle AlgorithmWCA2012خیرقویبله5متوسط
3Artificial Ecosystem-based OptimizationAEO2019خیربهترینبله2ساده
4
Math یا مبتنی بر ریاضیات1Hill ClimbingHC1993خیرضعیفخیر3ساده
2Sine Cosine AlgorithmSCA2016خیرقویخیر2ساده
3
Music یا مبتنی بر موزیک1Harmony SearchHS2001خیرقویخیر5ساده
2
Probabilistic یا مبتنی بر احتمال1Cross-Entropy MethodCEM1997خیرقویخیر4ساده
2
Dummy Algorithms یا الگوریتم های ساختگی1Pigeon-Inspired OptimizationPIO2014خیرقویخیر2متوسط
2Artificial Algae AlgorithmAAA2015خیرضعیفخیر5متوسط
3Rhiخیر Herd OptimizationRHO2018خیرقویبله6ساده
4Emperor Penguin OptimizerEPO2018خیرقویخیر2ساده
5Butterfly Optimization AlgorithmBOA2019خیرضعیفخیر6متوسط
6Sea Lion OptimizationSLO2019خیرقویبله2ساده
7Blue Monkey OptimizationBMO2019خیرضعیفخیر3متوسط
8Sandpiper Optimization AlgorithmSOA2020خیرضعیفخیر2ساده
9Black Widow OptimizationBWO2020خیرقویبله5متوسط

در ادامه مقاله مرتبط با هر الگوریتم بر اساس حروف الفبا لیست شده است

A
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.

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

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

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

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.

BeesA - Bees Algorithm . 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.

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

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.

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

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.

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.

BRO - Battle Royale Optimization. Rahkar Farshi, T. (2020). Battle royale optimization algorithm. Neural Computing and Applications, 1-19.

C
CA - Culture Algorithm . 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 . 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 . 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 . 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 . 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.

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.

DSA - Differential Search Algorithm . Civicioglu, P. (2012). Transforming geocentric cartesian coordinates to geodetic coordinates by using differential search algorithm. Computers & Geosciences, 46, 229-247.

DO - Dragonfly Optimization . 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 . 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.

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

EHO - Elephant Herding Optimization . 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 . 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.

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.

EO - Equilibrium Optimizer . Faramarzi, A., Heidarinejad, M., Stephens, B., & Mirjalili, S. (2019). Equilibrium optimizer: A novel optimization algorithm. Knowledge-Based Systems.

F
FireflyA - Firefly Algorithm . 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 . 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 . 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 . Chou, J.S. and Nguyen, N.M., 2020. FBI inspired meta-optimization. Applied Soft Computing, p.106339.

FOA - Fruit-fly Optimization Algorithm. Pan, W. T. (2012). A new fruit fly optimization algorithm: taking the financial distress model as an example. Knowledge-Based Systems, 26, 69-74.

G
GA - Genetic Algorithm . Holland, J. H. (1992). Genetic algorithms. Scientific american, 267(1), 66-73.

GWO - Grey Wolf Optimizer . Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in engineering software, 69, 46-61.

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

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.

GSKA - Gaining Sharing Knowledge-based Algorithm . 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.

H
HC - Hill Climbing . 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.

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.

HGS -- Hunger Games Search . 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 . 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 . 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 .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. 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.
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
MA - Memetic Algorithm . 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 . Mirjalili, S. (2015). Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-based systems, 89, 228-249.

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.

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.

NMRA - Nake Mole-rat Algorithm . Salgotra, R., & Singh, U. (2019). The naked mole-rat algorithm. Neural Computing and Applications, 31(12), 8837-8857.

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.

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

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.

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

SCA - Sine Cosine Algorithm . Mirjalili, S. (2016). SCA: a sine cosine algorithm for solving optimization problems. Knowledge-Based Systems, 96, 120-133.

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.

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.

SalpSO - Salp Swarm Optimization . 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 . 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.

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.

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.

SMA - Slime Mould Algorithm. 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.

SpaSA - Sparrow Search Algorithm . 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

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.

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VCS - Virus Colony Search . 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.
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WCA - Water Cycle Algorithm . 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 . 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.

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

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.

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

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

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.

PIO - Pigeon-Inspired Optimization . 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.

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.

SLO - Sea Lion Optimization . Masadeh, R., Mahafzah, B. A., & Sharieh, A. (2019). Sea Lion Optimization Algorithm. Sea, 10(5).

SOA - Sandpiper Optimization Algorithm . 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.

STOA - Sooty Tern Optimization Algorithm. 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
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