Evolutionary Computation Bestiary

Updated 2024-04-18

"Till now, madness has been thought a small island in an ocean of sanity. I am beginning to suspect that it is not an island at all but a continent." -- Machado de Assis, The Psychiatrist.

Important note (2 June 2023): we do not endorse or recommend the use of any of the methods listed here. In fact, we find most if not all of them quite ridiculous, and in many cases a useless wste of space. If you send us a message asking to have your own recently-published paper included here, you are publicly acknowledging that you have absolutely no idea what you're doing with your academic life, and that you're too disctracted to even realise that we're making fun of you. You have been warned.

Introduction

The field of meta-heuristic search algorithms has a long history of finding inspiration in natural systems. Starting from classics such as Genetic Algorithms and Ant Colony Optimization, the last two decades have witnessed a fireworks-style explosion (pun intended) of natural (and sometimes supernatural) heuristics - from Birds and Bees to Zombies and Reincarnation.

The goal of the Evolutionary Computation Bestiary is to catalog the, ermm... exuberance of the meta-heuristic "eco-system". We try to keep a list of the many different animals, plants, microbes, natural phenomena and supernatural activities that can be spotted in the wild lands of the metaphor-based computation literature.

While we personally believe that the literature could do with more mathematics and less marsupials, and that we, as a community, should grow past this metaphor-rich phase in our field's history (a bit like chemistry outgrew alchemy), please note that this list makes no claims about the scientific quality of the papers listed. The EC Bestiary puts classic works of the metaheuristics literature (e.g., GAs, ACO) and some that describe their methods in mostly metaphor-free language (e.g., JTF, CFO) side by side with others for which the scientific rigor is, to put it mildly, lacking. In short, it is not a Hall of Fame of algorithms - think of it more as The island of Doctor Moreau: a place with a few good creatures, but which are vastly outnumbered by mindless beasts.

Finally, if you know a metaphor-based method that is not listed here, or if you know of an earlier mention of a listed method, please see the bottom of the page on how to contribute!

The Bestiary

A

African Buffalo: Odili JB, Kahar MNM (2016). "Solving the Traveling Salesman's Problem Using the African Buffalo Optimization." Computational Intelligence and Neuroscience, 2016, 1-12. doi:10.1155/2016/1510256
Al-Biruni: M. El-kenawy E, A. Abdelhamid A, Ibrahim A, Mirjalili S, Khodadad N, A. Al duailij M, Ali Alhussan A, Sami Khafaga D (2023). "Al-Biruni Earth Radius (BER) Metaheuristic Search Optimization Algorithm." Computer Systems Science and Engineering, 45(2), 1917–1934. ISSN 0267-6192, doi:10.32604/csse.2023.032497
Algae: Uymaz SA, Tezel G, Yel E (2015). "Artificial algae algorithm (AAA) for nonlinear global optimization." Applied Soft Computing, 31, 153-171. doi:10.1016/j.asoc.2015.03.003
Ali Baba And The Forty Thieves: Braik M, Ryalat MH, Al-Zoubi H (2021). "A novel meta-heuristic algorithm for solving numerical optimization problems: Ali Baba and the forty thieves." Neural Computing and Applications, 34(1), 409–455. ISSN 1433-3058, doi:10.1007/s00521-021-06392-x
American Zebras: Mohapatra S, Mohapatra P (2023). "American zebra optimization algorithm for global optimization problems." Scientific Reports, 13(1). ISSN 2045-2322, doi:10.1038/s41598-023-31876-2
Amoeba: Wang H, Lu X, Zhang X, Wang Q, Deng Y (2014). "A Bio-Inspired Method for the Constrained Shortest Path Problem." The Scientific World Journal, 2014, 1-11. doi:10.1155/2014/271280
Amoeba: Plasmodium: Zhu L, Kim S, Hara M, Aono M (2018). "Remarkable problem-solving ability of unicellular amoeboid organism and its mechanism." Royal Society Open Science, 5(12), 180396. doi:10.1098/rsos.180396
Anarchic Society: Shayeghi H, Dadashpour J (2012). "Anarchic Society Optimization Based PID Control of an Automatic Voltage Regulator (AVR) System." Electrical and Electronic Engineering, 2(4), 199-207. doi:10.5923/j.eee.20120204.05
Andean Condors: Almonacid B, Soto R (2018). "Andean Condor Algorithm for cell formation problems." Natural Computing. doi:10.1007/s11047-018-9675-0
Anglerfish: Pook MF, Ramlan EI (2018). "The Anglerfish algorithm: a derivation of randomized incremental construction technique for solving the traveling salesman problem." Evolutionary Intelligence, 12(1), 11-20. doi:10.1007/s12065-018-0169-x
Animal Behavior: Crow/Wolf Synergies: Sassi M, Chelouah R (2023). "HHO-EAS: a new metaheuristic bio-inspired of the win–win hunting synergy between the two predators crow and wolf." Artificial Intelligence Review, 56(11), 12439–12504. ISSN 1573-7462, doi:10.1007/s10462-023-10428-0
Animal Behavior: Hunger Games: Yang Y, Chen H, Heidari AA, Gandomi AH (2021). "Hunger games search: Visions, conception, implementation, deep analysis, perspectives, and towards performance shifts." Expert Systems with Applications, 177, 114864. ISSN 0957-4174, doi:10.1016/j.eswa.2021.114864
Animal Behavior: Hunting: Naderi B, Khalili M, Khamseh AA (2014). "Mathematical models and a hunting search algorithm for the no-wait flowshop scheduling with parallel machines." International Journal of Production Research, 52(9), 2667-2681. doi:10.1080/00207543.2013.871389
Animal Behavior: Predation: Tilahun SL, Ong HC (2015). "Prey-Predator Algorithm: A New Metaheuristic Algorithm for Optimization Problems." International Journal of Information Technology & Decision Making, 14(06), 1331-1352. doi:10.1142/s021962201450031x
Animal Behavior: Searching: He S, Wu Q, Saunders J (2009). "Group Search Optimizer: An Optimization Algorithm Inspired by Animal Searching Behavior." IEEE Transactions on Evolutionary Computation, 13(5), 973-990. doi:10.1109/tevc.2009.2011992
Ant Colony: Maniezzo A (1992). "Distributed optimization by ant colonies." In Toward a Practice of Autonomous Systems: Proceedings of the First European Conference on Artificial Life, 134. Mit Press.
Ant Lion: Mirjalili S (2015). "The Ant Lion Optimizer." Advances in Engineering Software, 83, 80-98. doi:10.1016/j.advengsoft.2015.01.010
Antibodies: De Castro LN, Von Zuben FJ (2000). "The clonal selection algorithm with engineering applications." In Proceedings of GECCO, volume 2000, 36-39.
Aquilas: Abualigah L, Yousri D, Abd Elaziz M, Ewees AA, Al-qaness MA, Gandomi AH (2021). "Aquila Optimizer: A novel meta-heuristic optimization algorithm." Computers & Industrial Engineering, 157, 107250. ISSN 0360-8352, doi:10.1016/j.cie.2021.107250
Armadillos: Alsayyed O, Hamadneh T, Al-Tarawneh H, Alqudah M, Gochhait S, Leonova I, Malik OP, Dehghani M (2023). "Giant Armadillo Optimization: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems." Biomimetics, 8(8), 619. ISSN 2313-7673, doi:10.3390/biomimetics8080619
Artillery: Pijarski P, Kacejko P (2019). "A new metaheuristic optimization method: the algorithm of the innovative gunner (AIG)." Engineering Optimization, 51(12), 2049-2068. doi:10.1080/0305215x.2019.1565282
Axolotls: Villuendas-Rey Y, Velázquez-Rodríguez JL, Alanis-Tamez MD, Moreno-Ibarra M, Yáñez-Márquez C (2021). "Mexican Axolotl Optimization: A Novel Bioinspired Heuristic." Mathematics, 9(7), 781. ISSN 2227-7390, doi:10.3390/math9070781

B

Bachelors: Hu TC, Kahng AB, Tsao CA (1995). "Old Bachelor Acceptance: A New Class of Non-Monotone Threshold Accepting Methods." ORSA Journal on Computing, 7(4), 417-425. doi:10.1287/ijoc.7.4.417
Bacteria: Bacterial Chemotaxis: Muller S, Marchetto J, Airaghi S, Kournoutsakos P (2002). "Optimization based on bacterial chemotaxis." IEEE Transactions on Evolutionary Computation, 6(1), 16-29. doi:10.1109/4235.985689
Bacteria: Bacterial Foraging: Passino K (2002). "Biomimicry of bacterial foraging for distributed optimization and control." IEEE Control Systems Magazine, 22(3), 52-67. doi:10.1109/mcs.2002.1004010
Bacteria: Bacterial Swarming: Chu Y, Mi H, Liao H, Ji Z, Wu QH (2008). "A Fast Bacterial Swarming Algorithm for high-dimensional function optimization." In 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence). doi:10.1109/cec.2008.4631222
Bacteria: Magnetotactic Bacteria: Mo H, Xu L (2013). "Magnetotactic bacteria optimization algorithm for multimodal optimization." In 2013 IEEE Symposium on Swarm Intelligence (SIS). doi:10.1109/sis.2013.6615185
Bamboos: Pan J, Yue L, Chu S, Hu P, Yan B, Yang H (2023). "Binary Bamboo Forest Growth Optimization Algorithm for Feature Selection Problem." Entropy, 25(2), 314. ISSN 1099-4300, doi:10.3390/e25020314
Barnacles Mating: Sulaiman MH, Mustaffa Z, Saari MM, Daniyal H, Mohamad AJ, Othman MR, Mohamed MR (2019). "Barnacles Mating Optimizer Algorithm for Optimization." In Proceedings of the 10th National Technical Seminar on Underwater System Technology 2018, 211-218. Springer Singapore. doi:10.1007/978-981-13-3708-6_18
Bats: Yang X (2010). "A new metaheuristic bat-inspired algorithm." In Nature inspired cooperative strategies for optimization (NICSO 2010), 65-74. Springer.
Battle Royale Game: Farshi TR (2020). "Battle royale optimization algorithm." Neural Computing and Applications, 33(4), 1139-1157. doi:10.1007/s00521-020-05004-4
Beans: Seeds: Feng T, Xie Q, Hu H, Song L, Cui C, Zhang X (2015). "Bean Optimization Algorithm Based on Negative Binomial Distribution." In Lecture Notes in Computer Science, 82–88. ISBN 9783319204666, doi:10.1007/978-3-319-20466-6_9
Beans: Transmission: Zhang X, Sun B, Mei T, Wang R (2010). "Post-disaster restoration based on fuzzy preference relation and Bean Optimization Algorithm." In 2010 IEEE Youth Conference on Information, Computing and Telecommunications. doi:10.1109/ycict.2010.5713097
Bees: Bee Colonies: Teodorovic D, Lucic P, Markovic G, Orco MD (2006). "Bee Colony Optimization: Principles and Applications." In 2006 8th Seminar on Neural Network Applications in Electrical Engineering. doi:10.1109/neurel.2006.341200
Bees: Bee Colonies 2: Karaboga D, Basturk B (2007). "Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems." In Lecture Notes in Computer Science, 789-798. Springer Berlin Heidelberg. doi:10.1007/978-3-540-72950-1_77
Bees: Bumblebees: Comellas F, Martinez-Navarro J (2009). "Bumblebees." In Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation - GEC \textquotesingle09. doi:10.1145/1543834.1543949
Bees: Honey Bee Marriages: Abbass H (2001). "MBO: marriage in honey bees optimization-a Haplometrosis polygynous swarming approach." In Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546). doi:10.1109/cec.2001.934391
Bees: Queen Bees: Jung SH (2003). "Queen-bee evolution for genetic algorithms." Electronics Letters, 39(6), 575. doi:10.1049/el:20030383
Beetles: Dark Beetles: Kallioras NA, Lagaros ND, Avtzis DN (2018). "Pity beetle algorithm — A new metaheuristic inspired by the behavior of bark beetles." Advances in Engineering Software, 121, 147-166. doi:10.1016/j.advengsoft.2018.04.007
Beetles: Longicorn Beetles: Han X, Du X, Yu P (2020). "ATLA: A novel metaheuristic optimization algorithm inspired by the mating search behavior of longicorn beetles in the nature." IOP Conference Series: Materials Science and Engineering, 782(5), 052028. ISSN 1757-899X, doi:10.1088/1757-899x/782/5/052028
Big Bang: Erol OK, Eksin I (2006). "A new optimization method: Big Bang—Big Crunch." Advances in Engineering Software, 37(2), 106-111. doi:10.1016/j.advengsoft.2005.04.005
Biogeography: Simon D (2008). "Biogeography-Based Optimization." IEEE Transactions on Evolutionary Computation, 12(6), 702-713. doi:10.1109/tevc.2008.919004
Birds: Bird Migrations: Duman E, Uysal M, Alkaya AF (2012). "Migrating Birds Optimization: A new metaheuristic approach and its performance on quadratic assignment problem." Information Sciences, 217, 65-77. doi:10.1016/j.ins.2012.06.032
Birds: Birds Mating: Askarzadeh A (2014). "Bird mating optimizer: An optimization algorithm inspired by bird mating strategies." Communications in Nonlinear Science and Numerical Simulation, 19(4), 1213-1228. doi:10.1016/j.cnsns.2013.08.027
Birds: Escaping Strategies: Shahrouzi M, Kaveh A (2022). "An efficient derivative-free optimization algorithm inspired by avian life-saving manoeuvres." Journal of Computational Science, 57, 101483. ISSN 1877-7503, doi:10.1016/j.jocs.2021.101483
Birds: Hitchcock Birds: Morais RG, Nedjah N, Mourelle LM (2019). "A novel metaheuristic inspired by Hitchcock birds' behavior for efficient optimization of large search spaces of high dimensionality." Soft Computing, 24(8), 5633-5655. doi:10.1007/s00500-019-04102-3
Bison: Kazikova A, Pluhacek M, Senkerik R, Viktorin A (2018). "Proposal of a New Swarm Optimization Method Inspired in Bison Behavior." In Recent Advances in Soft Computing, 146-156. Springer International Publishing. doi:10.1007/978-3-319-97888-8_13
Black Holes: Hatamlou A (2013). "Black hole: A new heuristic optimization approach for data clustering." Information Sciences, 222, 175-184. doi:10.1016/j.ins.2012.08.023
Black Widow: Hayyolalam V, Kazem AAP (2020). "Black Widow Optimization Algorithm: A novel meta-heuristic approach for solving engineering optimization problems." Engineering Applications of Artificial Intelligence, 87, 103249. doi:10.1016/j.engappai.2019.103249
Bonobos: Das AK, Nikum AK, Krishnan SV, Pratihar DK (2020). "Multi-objective Bonobo Optimizer (MOBO): an intelligent heuristic for multi-criteria optimization." Knowledge and Information Systems, 62(11), 4407-4444. doi:10.1007/s10115-020-01503-x
Brainstorming: Shi Y (2011). "An Optimization Algorithm Based on Brainstorming Process." International Journal of Swarm Intelligence Research, 2(4), 35-62. doi:10.4018/ijsir.2011100103
BrunsVigia Flower: Ghaemidizaji M, Dadkhah C, Leung H (2018). "A New Optimization Algorithm Based on the Behavior of BrunsVigia Flower." In 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC). doi:10.1109/smc.2018.00055
Buses: Bodaghi M, Samieefar K (2018). "Meta-heuristic bus transportation algorithm." Iran Journal of Computer Science. doi:10.1007/s42044-018-0025-2
Butterflies: Monarch Butterflies: Wang G, Deb S, Cui Z (2015). "Monarch butterfly optimization." Neural Computing and Applications. doi:10.1007/s00521-015-1923-y
Butterflies: Regular Butterflies: Arora S, Singh S (2018). "Butterfly optimization algorithm: a novel approach for global optimization." Soft Computing. doi:10.1007/s00500-018-3102-4
Buzzards: Arshaghi A, Ashourian M, Ghabeli L (2019). "Buzzard Optimization Algorithm: A Nature-Inspired Metaheuristic Algorithm." Majlesi Journal of Electrical Engineering, 13(3), 83-98. http://mjee.iaumajlesi.ac.ir/index/index.php/ee/article/view/3363.

C

Camels: M. K. Ibrahim RSA (2016). "Novel Optimization Algorithm Inspired by Camel Traveling Behavior." Iraq J. Electrical and Electronic Engineering, 12(2), 167-177. ISSN 18145892,
Cancers: Tang D, Dong S, Jiang Y, Li H, Huang Y (2015). "ITGO: Invasive tumor growth optimization algorithm." Applied Soft Computing, 36, 670-698. doi:10.1016/j.asoc.2015.07.045
Cats: Behaviors: Chu S, Tsai P, Pan J (2006). "Cat Swarm Optimization." In Lecture Notes in Computer Science, 854-858. Springer Berlin Heidelberg. doi:10.1007/978-3-540-36668-3_94
Cats: Sand Cats: Seyyedabbasi A, Kiani F (2022). "Sand Cat swarm optimization: a nature-inspired algorithm to solve global optimization problems." Engineering with Computers, 39(4), 2627–2651. ISSN 1435-5663, doi:10.1007/s00366-022-01604-x
Central Force: Formato RA (2007). "CENTRAL FORCE OPTIMIZATION: A NEW METAHEURISTIC WITH APPLICATIONS IN APPLIED ELECTROMAGNETICS." Progress In Electromagnetics Research, 77, 425-491. doi:10.2528/pier07082403
Chameleons: Braik MS (2021). "Chameleon Swarm Algorithm: A bio-inspired optimizer for solving engineering design problems." Expert Systems with Applications, 174, 114685. doi:10.1016/j.eswa.2021.114685
Charged Systems: Kaveh A, Talatahari S (2010). "A novel heuristic optimization method: charged system search." Acta Mechanica, 213(3-4), 267-289. doi:10.1007/s00707-009-0270-4
Cheetah: Klein CE, Mariani V, dos Santos Coelho L (2018). "Cheetah Based Optimization Algorithm: A Novel Swarm Intelligence Paradigm." In Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning.
Chemical Reactions: Alatas B (2011). "ACROA: Artificial Chemical Reaction Optimization Algorithm for global optimization." Expert Systems with Applications, 38(10), 13170-13180. doi:10.1016/j.eswa.2011.04.126
Chickens: Chicken Laying Eggs: Hosseini E (2017). "Laying Chicken Algorithm: A New Meta-Heuristic Approach to Solve Continuous Programming Problems." Journal of Applied & Computational Mathematics, 06(01). doi:10.4172/2168-9679.1000344
Chickens: Chicken Swarms: Meng X, Liu Y, Gao X, Zhang H (2014). "A New Bio-inspired Algorithm: Chicken Swarm Optimization." In Lecture Notes in Computer Science, 86-94. Springer International Publishing. doi:10.1007/978-3-319-11857-4_10
Children's Drawings: Ameen AA, Rashid TA, Askar S (2023). "CDDO–HS: Child Drawing Development Optimization–Harmony Search Algorithm." Applied Sciences, 13(9), 5795. ISSN 2076-3417, doi:10.3390/app13095795
Chimps: Khishe M, Mosavi M (2020). "Chimp optimization algorithm." Expert Systems with Applications, 149, 113338. ISSN 0957-4174, doi:10.1016/j.eswa.2020.113338
Clouds: YAN G, HAO Z (2013). "A NOVEL OPTIMIZATION ALGORITHM BASED ON ATMOSPHERE CLOUDS MODEL." International Journal of Computational Intelligence and Applications, 12(01), 1350002. doi:10.1142/s1469026813500028
Cockroaches: Obagbuwa IC, Adewumi AO (2014). "An Improved Cockroach Swarm Optimization." The Scientific World Journal, 2014, 1-13. doi:10.1155/2014/375358
Colliding Bodies: Kaveh A, Mahdavi V (2014). "Colliding bodies optimization: A novel meta-heuristic method." Computers & Structures, 139, 18-27. doi:10.1016/j.compstruc.2014.04.005
Community of scientists: Alfredo M, Valentino S (2012). "Community of scientist optimization: An autonomy oriented approach to distributed optimization." AI Communications, 25(2), 157–172. ISSN 0921-7126, doi:10.3233/AIC-2012-0526
Consultants: Iordache S (2010). "Consultant-guided search." In Proceedings of the 12th annual conference on Genetic and evolutionary computation - GECCO \textquotesingle10. doi:10.1145/1830483.1830526
Coral Reefs: Salcedo-Sanz S, Ser JD, Landa-Torres I, Gil-López S, Portilla-Figueras JA (2014). "The Coral Reefs Optimization Algorithm: A Novel Metaheuristic for Efficiently Solving Optimization Problems." The Scientific World Journal, 2014, 1-15. doi:10.1155/2014/739768
COVID19: Containment: Emami H (2022). "Anti-coronavirus optimization algorithm." Soft Computing, 26(11), 4991–5023. ISSN 1433-7479, doi:10.1007/s00500-022-06903-5
COVID19: Distancing: Hosseini E, Ghafoor KZ, Sadiq AS, Guizani M, Emrouznejad A (2020). "COVID-19 Optimizer Algorithm, Modeling and Controlling of Coronavirus Distribution Process." IEEE Journal of Biomedical and Health Informatics, 24(10), 2765-2775. doi:10.1109/jbhi.2020.3012487
COVID19: Propagation: Martínez-Álvarez F, Asencio-Cortés G, Torres JF, Gutiérrez-Avilés D, Melgar-García L, Pérez-Chacón R, Rubio-Escudero C, Riquelme JC, Troncoso A (2020). "Coronavirus Optimization Algorithm: A Bioinspired Metaheuristic Based on the COVID-19 Propagation Model." Big Data, 8(4), 308–322. ISSN 2167-647X, doi:10.1089/big.2020.0051
Coyotes: Pierezan J, Coelho LDS (2018). "Coyote Optimization Algorithm: A New Metaheuristic for Global Optimization Problems." In 2018 IEEE Congress on Evolutionary Computation (CEC), 1-8. IEEE.
Crab: Chifu VR, Salomie I, Chifu ES, Negrean A, Jeflea H, Antal M (2014). "Crab mating optimization algorithm." In 2014 18th International Conference on System Theory, Control and Computing (ICSTCC). doi:10.1109/icstcc.2014.6982441
Crickets: Canayaz M, Karci A (2015). "Cricket behaviour-based evolutionary computation technique in solving engineering optimization problems." Applied Intelligence, 44(2), 362–376. ISSN 1573-7497, doi:10.1007/s10489-015-0706-6
Crows: Askarzadeh A (2016). "A novel metaheuristic method for solving constrained engineering optimization problems: Crow search algorithm." Computers & Structures, 169, 1-12. doi:10.1016/j.compstruc.2016.03.001
Crows: Chaotic: Rizk-Allah RM, Hassanien AE, Bhattacharyya S (2018). "Chaotic crow search algorithm for fractional optimization problems." Applied Soft Computing, 71, 1161-1175. doi:10.1016/j.asoc.2018.03.019
Crystal Energy: Feng X, Ma M, Yu H (2014). "Crystal Energy Optimization Algorithm." Computational Intelligence, 32(2), 284-322. doi:10.1111/coin.12053
Cuckoos: Yang X, Deb S (2009). "Cuckoo Search via L&#x00E9$\mathsemicolon$vy flights." In 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC). doi:10.1109/nabic.2009.5393690

D

Dandelions: Zhao S, Zhang T, Ma S, Chen M (2022). "Dandelion Optimizer: A nature-inspired metaheuristic algorithm for engineering applications." Engineering Applications of Artificial Intelligence, 114, 105075. ISSN 0952-1976, doi:10.1016/j.engappai.2022.105075
Deer: Scottish Red Deer: Fard AF, Hajiaghaei-Keshteli M (2016). "Red Deer Algorithm (RDA); A New Optimization Algorithm Inspired by Red Deers’ Mating." In International Conference on Industrial Engineering, IEEE.,(2016 e), 33-34.
Dendritic Cells: Greensmith J, Aickelin U, Cayzer S (2005). "Introducing dendritic cells as a novel immune-inspired algorithm for anomaly detection." In International Conference on Artificial Immune Systems, 153-167. Springer.
Dice Games: DEHGHANI M, MONTAZERI Z, MALIK OP (2019). "DGO: Dice Game Optimizer." GAZI UNIVERSITY JOURNAL OF SCIENCE, 32(3), 871-882. doi:10.35378/gujs.484643
Dogs: African Wild Dogs: Subramanian C, Sekar A, Subramanian K (2013). "A New Engineering Optimization Method: African Wild Dog Algorithm." International Journal of Soft Computing, 8(3).
Dogs: Australian Dingo Dogs: Peraza-Vázquez H, Peña-Delgado AF, Echavarría-Castillo G, Morales-Cepeda AB, Velasco-Álvarez J, Ruiz-Perez F (2021). "A Bio-Inspired Method for Engineering Design Optimization Inspired by Dingoes Hunting Strategies." Mathematical Problems in Engineering, 2021, 1–19. ISSN 1024-123X, doi:10.1155/2021/9107547
Dogs: Border Collie: Dutta T, Bhattacharyya S, Dey S, Platos J (2020). "Border Collie Optimization." IEEE Access, 8, 109177-109197. doi:10.1109/access.2020.2999540
Dolphins: Dolphin Echolocation: Kaveh A, Farhoudi N (2013). "A new optimization method: Dolphin echolocation." Advances in Engineering Software, 59, 53-70. doi:10.1016/j.advengsoft.2013.03.004
Dolphins: Dolphin Partners: Shiqin Y, Jianjun J, Guangxing Y (2009). "A Dolphin Partner Optimization." In 2009 WRI Global Congress on Intelligent Systems. doi:10.1109/gcis.2009.464
Dolphins: Dolphin Swarms: Wu T, Yao M, Yang J (2016). "Dolphin swarm algorithm." Frontiers of Information Technology & Electronic Engineering, 17(8), 717–729. ISSN 2095-9230, doi:10.1631/fitee.1500287
Donkeys: Dehghani M, Mardaneh M, Malik OP, NouraeiPour SM (2019). "DTO: Donkey Theorem Optimization." In 2019 27th Iranian Conference on Electrical Engineering (ICEE). doi:10.1109/iraniancee.2019.8786601
Dragonflies: Mirjalili S (2015). "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. doi:10.1007/s00521-015-1920-1
Duelists: Biyanto TR, Fibrianto HY, Nugroho G, Hatta AM, Listijorini E, Budiati T, Huda H (2016). "Duelist Algorithm: An Algorithm Inspired by How Duelist Improve Their Capabilities in a Duel." In Tan Y, Shi Y, Niu B (eds.), Advances in Swarm Intelligence, 39-47. ISBN 978-3-319-41000-5.
Dwarf Mongooses: Agushaka JO, Ezugwu AE, Abualigah L (2022). "Dwarf Mongoose Optimization Algorithm." Computer Methods in Applied Mechanics and Engineering, 391, 114570. ISSN 0045-7825, doi:10.1016/j.cma.2022.114570

E

Eagles: Bald Eagles: Alsattar HA, Zaidan AA, Zaidan BB (2019). "Novel meta-heuristic bald eagle search optimisation algorithm." Artificial Intelligence Review, 53(3), 2237–2264. ISSN 1573-7462, doi:10.1007/s10462-019-09732-5
Eagles: Golden Eagles: Mohammadi-Balani A, Dehghan Nayeri M, Azar A, Taghizadeh-Yazdi M (2021). "Golden eagle optimizer: A nature-inspired metaheuristic algorithm." Computers & Industrial Engineering, 152, 107050. ISSN 0360-8352, doi:10.1016/j.cie.2020.107050
Eagles: Lévy Walk: Yang X, Deb S (2010). "Eagle Strategy Using Lévy Walk and Firefly Algorithms for Stochastic Optimization." In Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), 101-111. Springer Berlin Heidelberg. doi:10.1007/978-3-642-12538-6_9
Earthworms: Wang G, Deb S, Coelho LDS (2015). "Earthworm optimization algorithm: a bio-inspired metaheuristic algorithm for global optimization problems." International Journal of Bio-Inspired Computation, 7, 1-23.
Ebola: Oyelade ON, Ezugwu AE, Mohamed TIA, Abualigah L (2022). "Ebola Optimization Search Algorithm: A New Nature-Inspired Metaheuristic Optimization Algorithm." IEEE Access, 10, 16150–16177. ISSN 2169-3536, doi:10.1109/access.2022.3147821
Ecogeography: Zheng Y, Ling H, Xue J (2014). "Ecogeography-based optimization: Enhancing biogeography-based optimization with ecogeographic barriers and differentiations." Computers & Operations Research, 50, 115-127. doi:10.1016/j.cor.2014.04.013
Ecology: Parpinelli RS, Lopes HS (2011). "An eco-inspired evolutionary algorithm applied to numerical optimization." In 2011 Third World Congress on Nature and Biologically Inspired Computing. doi:10.1109/nabic.2011.6089631
Electromagnetism: Cuevas E, Oliva D, Zaldivar D, Pérez-Cisneros M, Sossa H (2012). "Circle detection using electro-magnetism optimization." Information Sciences, 182(1), 40-55. doi:10.1016/j.ins.2010.12.024
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Wind: Bayraktar Z, Komurcu M, Werner DH (2010). "Wind Driven Optimization (WDO): A novel nature-inspired optimization algorithm and its application to electromagnetics." In 2010 IEEE Antennas and Propagation Society International Symposium. doi:10.1109/aps.2010.5562213
Wingsuit: Yang J, Zhang Y, Wang Z, Todo Y, Lu B, Gao S (2021). "A Cooperative Coevolution Wingsuit Flying Search Algorithm with Spherical Evolution." International Journal of Computational Intelligence Systems, 14(1). ISSN 1875-6883, doi:10.1007/s44196-021-00030-z
Wolves: Grey Wolves: Mirjalili S, Mirjalili SM, Lewis A (2014). "Grey Wolf Optimizer." Advances in Engineering Software, 69, 46-61. doi:10.1016/j.advengsoft.2013.12.007
Wolves: Wolf Packs: Yang C, Tu X, Chen J (2007). "Algorithm of Marriage in Honey Bees Optimization Based on the Wolf Pack Search." In The 2007 International Conference on Intelligent Pervasive Computing (IPC 2007). doi:10.1109/ipc.2007.104
Wolves: Wolves: Tang R, Fong S, Yang X, Deb S (2012). "Wolf search algorithm with ephemeral memory." In Seventh International Conference on Digital Information Management (ICDIM 2012). doi:10.1109/icdim.2012.6360147
Worms: Arnaout J (2014). "Worm optimization: a novel optimization algorithm inspired by C. Elegans." In Proceedings of the 2014 International Conference on Industrial Engineering and Operations Management, 2499-2505.

X

Y

Yellow Saddle Goatfish: Zald'\ivar D, Morales B, Rodr'\iguez A, Valdivia-G A, Cuevas E, Pérez-Cisneros M (2018). "A novel bio-inspired optimization model based on Yellow Saddle Goatfish behavior." Biosystems, 174, 1-21. doi:10.1016/j.biosystems.2018.09.007
Yin-Yang Pairs: Punnathanam V, Kotecha P (2016). "Yin-Yang-pair Optimization: A novel lightweight optimization algorithm." Engineering Applications of Artificial Intelligence, 54, 62-79. doi:10.1016/j.engappai.2016.04.004

Z

Zebras: Trojovska E, Dehghani M, Trojovsky P (2022). "Zebra Optimization Algorithm: A New Bio-Inspired Optimization Algorithm for Solving Optimization Algorithm." IEEE Access, 10, 49445–49473. ISSN 2169-3536, doi:10.1109/access.2022.3172789
Zombies: Nguyen HT, Bhanu B (2012). "Zombie Survival Optimization: A swarm intelligence algorithm inspired by zombie foraging." In Pattern Recognition (ICPR), 2012 21st International Conference on, 987-990. IEEE.

Maintainers

("the Zoo Keepers")

Claus Aranha, Tsukuba University, Japan.
Felipe Campelo, Universidade Federal de Minas Gerais (UFMG), Brazil.

Contributors

(at least one contribution to the bestiary - in terms of adding a method to the list, not inventing it!)

Adré Steyn - University of Stellenbosch, South Africa
Alberto Franzin - Université Libre de Bruxelles, Belgium
Alexander J. Benavides - Universidad Nacional de San Agustín de Arequipa (UNSA), Perú
Alexander Magazinov, Tel Aviv University, Israel
Anand Subramanian - UFPB, Brazil
André Maravilha - UFMG, Brazil
Carlos Fonseca - University of Coimbra, Portugal
Ciniro Nametala - UFMG, Brazil
Christian L. Camacho Villalón - ULB, Brussels
Daniel Palhazi Cuervo - Ascenium, Norway
Denis Pallez - University Côte d'Azur, France
Eduardo Hauck - UFJF, Brazil
Fabio Daolio - University of Stirling, Scotland UK
Fernanda Takahashi - UFMG, Brazil
Fernando Otero - University of Kent, England UK
Fillipe Goulart - UFMG, Brazil
Federico Pagnozzi - Université Libre de Bruxelles, Belgium
Krystian Lapa - Institute of Computational Intelligence, Poland
Iago Augusto de Carvalho - Universidade Federal de Alfenas, Brazil
Iztok Fister Jr. - University of Maribor, Slovenia
Jakub Grabski - Poznan University of Technology, Poland
James Brookhouse - University of Kent, England UK
James McDermott - University of Galway, Ireland
Juan Carlos Chacon-Hurtado - TU Delft, Netherlands
Joao Duro - University of Sheffield, England UK
Joaquín Antonio Pacheco Bonrostro - University of Burgos, Spain
Kenneth Sörensen - University of Antwerp, Belgium
Konstantinos Zervoudakis - Technical University of Crete, Greece
Koen van der Blom - Leiden University, Netherlands
Lars Magnus Hvattum - Molde University College, Norway
Leandro Santos Coelho - UFPR, Brazil
Leonardo Goliatt da Fonseca - Universidade Federal de Juiz de Fora, Brazil
Luís Correia - Universidade de Lisboa, Portugal
Marcelo Maia - Federal University of Uberlândia, Brazil
Marco Mollinetti - University of Tsukuba, Japan
Marco Pranzo - Università di Siena, Italy
Marcus Ritt - UFRGS, Brazil
Marc Sevaux - Université Bretagne-Sud, France
Michał Okulewicz - Politechnika Warszawska, Poland
Michael Lones - Heriot-Watt University, Scotland
Nadarajen Veerapen - University of Stirling, Scotland UK
Nguyen Tri Hai - Chung-Ang University, South Korea
Nuno Rodrigues - University of Lisbon, Lisbon
Owein Thuillier - Université Bretagne-Sud, France
Paul Rubin - Michigan State University, USA
Peter Lewis - Aston University, UK
Pieter Leyman - Ghent University, Belgium
Rafael Stubs Parpinelli - Universidade do Estado de Santa Catarina (UDESC), Brasil
Robin Purshouse - University of Sheffield, England UK
Romain Billot - IMT Atlantique, France
Rubén Ruiz - Universitat Politècnica de València, Spain
Ruud Koot - Universiteit Utrecht, The Netherlands
Sara Silva - University of Lisbon
Sander - Leiden University, Netherlands
senorramirez
Sergio A. Rojas - Universidad Distrital de Bogotá, Colombia
Silvano Martello - University of Bologna, Italy
Stefan Voß - Universität Hamburg, Germany
Thomas Jacob Riis Stidsen - Danmarks Tekniske Universitet, Denmark
Thomas Stützle - Université Libre de Bruxelles, Belgium
Tushar Semwal - IIT Guwahati, India
Yuri Lavinas - University of Tsukuba, Brazil

How to Contribute

If you know a paper that should belong to this list, please send an e-mail to either Claus or Felipe, or report an issue on our Github repo. The criteria for inclusion are quite simple:

the work must be in a peer reviewed publication (journal or conference);
the title or abstract must name the algorithm after the natural (or supernatural) metaphor on which it was based;

It is also important to highlight that only the earliest known mention for each metaphor is included.

More Info:

If you liked this list, you should read the paper "Metaheuristic: The Metaphor Exposed", by Kenneth Söresen
Need inspiration for your next Bioinspired algorithm? Check Marco Scirea and Julian Togelius' Daily Bio-heuristics bot.
Some of the algorithms listed here were found in a list compiled by Iztok Fister Jr. et al., which is available here. Iztok also recently published this paper reflecting on the proliferation of metaphors in EC research.
A fantastic parody of this whole metaphor craze can be read here. Highly recommended!

License:

This work is licensed under the Creative Commons CC BY-NC-SA 4.0 license (Attribution Non-Commercial Share Alike International License version 4.0): http://creativecommons.org/licenses/by-nc-sa/4.0/

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Evolutionary Computation Bestiary

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Evolutionary Computation Bestiary

Introduction

The Bestiary

A

B

C

D

E

F

G

H

I

J

K

L

M

N

O

P

Q

R

S

T

U

V

W

X

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