Evolutionary Computation Bestiary

Updated 2019-03-28

"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.

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 and Kahar MNM (2016). “Solving the Traveling Salesman's Problem Using the African Buffalo Optimization.” Computational Intelligence and Neuroscience, 2016, pp. 1-12. doi: 10.1155/2016/1510256
Algae: Uymaz SA, Tezel G and Yel E (2015). “Artificial algae algorithm (AAA) for nonlinear global optimization.” Applied Soft Computing, 31, pp. 153-171. doi: 10.1016/j.asoc.2015.03.003
Amoeba: Wang H, Lu X, Zhang X, Wang Q and Deng Y (2014). “A Bio-Inspired Method for the Constrained Shortest Path Problem.” The Scientific World Journal, 2014, pp. 1-11. doi: 10.1155/2014/271280
Amoeba: Plasmodium: Zhu L, Kim S, Hara M and Aono M (2018). “Remarkable problem-solving ability of unicellular amoeboid organism and its mechanism.” Royal Society Open Science, 5(12), pp. 180396. doi: 10.1098/rsos.180396
Anarchic Society: Shayeghi H and Dadashpour J (2012). “Anarchic Society Optimization Based PID Control of an Automatic Voltage Regulator (AVR) System.” Electrical and Electronic Engineering, 2(4), pp. 199-207. doi: 10.5923/j.eee.20120204.05
Andean Condors: Almonacid B and Soto R (2018). “Andean Condor Algorithm for cell formation problems.” Natural Computing. doi: 10.1007/s11047-018-9675-0
Animal Behavior: Hunting: Naderi B, Khalili M and 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), pp. 2667-2681. doi: 10.1080/00207543.2013.871389
Animal Behavior: Predation: Tilahun SL and Ong HC (2015). “Prey-Predator Algorithm: A New Metaheuristic Algorithm for Optimization Problems.” International Journal of Information Technology & Decision Making, 14(06), pp. 1331-1352. doi: 10.1142/s021962201450031x
Animal Behavior: Searching: He S, Wu Q and Saunders J (2009). “Group Search Optimizer: An Optimization Algorithm Inspired by Animal Searching Behavior.” IEEE Transactions on Evolutionary Computation, 13(5), pp. 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, pp. 134. Mit Press.
Antibodies: De Castro LN and Von Zuben FJ (2000). “The clonal selection algorithm with engineering applications.” In Proceedings of GECCO, volume 2000, pp. 36-39.
Ant Lion: Mirjalili S (2015). “The Ant Lion Optimizer.” Advances in Engineering Software, 83, pp. 80-98. doi: 10.1016/j.advengsoft.2015.01.010

B

Bachelors: Hu TC, Kahng AB and Tsao CA (1995). “Old Bachelor Acceptance: A New Class of Non-Monotone Threshold Accepting Methods.” ORSA Journal on Computing, 7(4), pp. 417-425. doi: 10.1287/ijoc.7.4.417
Bacteria: Bacterial Chemotaxis: Muller S, Marchetto J, Airaghi S and Kournoutsakos P (2002). “Optimization based on bacterial chemotaxis.” IEEE Transactions on Evolutionary Computation, 6(1), pp. 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), pp. 52-67. doi: 10.1109/mcs.2002.1004010
Bacteria: Bacterial Swarming: Chu Y, Mi H, Liao H, Ji Z and 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 and Xu L (2013). “Magnetotactic bacteria optimization algorithm for multimodal optimization.” In 2013 IEEE Symposium on Swarm Intelligence (SIS). doi: 10.1109/sis.2013.6615185
Bats: Yang X (2010). “A new metaheuristic bat-inspired algorithm.” In Nature inspired cooperative strategies for optimization (NICSO 2010), pp. 65-74. Springer.
Bees: Bee Colonies: Teodorovic D, Lucic P, Markovic G and 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: Bumblebees: Comellas F and 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), pp. 575. doi: 10.1049/el:20030383
Beetles: Kallioras NA, Lagaros ND and Avtzis DN (2018). “Pity beetle algorithm \textendash A new metaheuristic inspired by the behavior of bark beetles.” Advances in Engineering Software, 121, pp. 147-166. doi: 10.1016/j.advengsoft.2018.04.007
Big Bang: Erol OK and Eksin I (2006). “A new optimization method: Big Bang\textendashBig Crunch.” Advances in Engineering Software, 37(2), pp. 106-111. doi: 10.1016/j.advengsoft.2005.04.005
Biogeography: Simon D (2008). “Biogeography-Based Optimization.” IEEE Transactions on Evolutionary Computation, 12(6), pp. 702-713. doi: 10.1109/tevc.2008.919004
Birds: Bird Migrations: Duman E, Uysal M and Alkaya AF (2012). “Migrating Birds Optimization: A new metaheuristic approach and its performance on quadratic assignment problem.” Information Sciences, 217, pp. 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), pp. 1213-1228. doi: 10.1016/j.cnsns.2013.08.027
Bison: Kazikova A, Pluhacek M, Senkerik R and Viktorin A (2018). “Proposal of a New Swarm Optimization Method Inspired in Bison Behavior.” In Recent Advances in Soft Computing, pp. 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, pp. 175-184. doi: 10.1016/j.ins.2012.08.023
Blind Naked Mole Rats: Taherdangkoo M, Shirzadi MH, Yazdi M and Bagheri MH (2013). “A robust clustering method based on blind, naked mole-rats (BNMR) algorithm.” Swarm and Evolutionary Computation, 10, pp. 1-11. doi: 10.1016/j.swevo.2013.01.001
Brainstorming: Shi Y (2011). “An Optimization Algorithm Based on Brainstorming Process.” International Journal of Swarm Intelligence Research, 2(4), pp. 35-62. doi: 10.4018/ijsir.2011100103
Buses: Bodaghi M and 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 and Cui Z (2015). “Monarch butterfly optimization.” Neural Computing and Applications. doi: 10.1007/s00521-015-1923-y
Butterflies: Regular Butterflies: Arora S and Singh S (2018). “Butterfly optimization algorithm: a novel approach for global optimization.” Soft Computing. doi: 10.1007/s00500-018-3102-4

C

Camels: M. K. Ibrahim RSA (2016). “Novel Optimization Algorithm Inspired by Camel Traveling Behavior.” Iraq J. Electrical and Electronic Engineering, 12(2), pp. 167-177. ISSN 18145892, <URL: https://www.iasj.net/iasj?func=article&aId=118375>.
Cancers: Tang D, Dong S, Jiang Y, Li H and Huang Y (2015). “ITGO: Invasive tumor growth optimization algorithm.” Applied Soft Computing, 36, pp. 670-698. doi: 10.1016/j.asoc.2015.07.045
Cats: Chu S, Tsai P and Pan J (2006). “Cat Swarm Optimization.” In Lecture Notes in Computer Science, pp. 854-858. Springer Berlin Heidelberg. doi: 10.1007/978-3-540-36668-3_94
Central Force: Formato RA (2007). “CENTRAL FORCE OPTIMIZATION: A NEW METAHEURISTIC WITH APPLICATIONS IN APPLIED ELECTROMAGNETICS.” Progress In Electromagnetics Research, 77, pp. 425-491. doi: 10.2528/pier07082403
Charged Systems: Kaveh A and Talatahari S (2010). “A novel heuristic optimization method: charged system search.” Acta Mechanica, 213(3-4), pp. 267-289. doi: 10.1007/s00707-009-0270-4
Cheetah: Klein CE, Mariani V and 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), pp. 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 and Zhang H (2014). “A New Bio-inspired Algorithm: Chicken Swarm Optimization.” In Lecture Notes in Computer Science, pp. 86-94. Springer International Publishing. doi: 10.1007/978-3-319-11857-4_10
Clouds: YAN G and HAO Z (2013). “A NOVEL OPTIMIZATION ALGORITHM BASED ON ATMOSPHERE CLOUDS MODEL.” International Journal of Computational Intelligence and Applications, 12(01), pp. 1350002. doi: 10.1142/s1469026813500028
Cockroaches: Obagbuwa IC and Adewumi AO (2014). “An Improved Cockroach Swarm Optimization.” The Scientific World Journal, 2014, pp. 1-13. doi: 10.1155/2014/375358
Colliding Bodies: Kaveh A and Mahdavi V (2014). “Colliding bodies optimization: A novel meta-heuristic method.” Computers & Structures, 139, pp. 18-27. doi: 10.1016/j.compstruc.2014.04.005
Community of scientists: Alfredo M and Valentino S (2012). “Community of scientist optimization: An autonomy oriented approach to distributed optimization.” AI Communications, 25(2), pp. 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 and Portilla-Figueras JA (2014). “The Coral Reefs Optimization Algorithm: A Novel Metaheuristic for Efficiently Solving Optimization Problems.” The Scientific World Journal, 2014, pp. 1-15. doi: 10.1155/2014/739768
Coyotes: Pierezan J and Coelho LDS (2018). “Coyote Optimization Algorithm: A New Metaheuristic for Global Optimization Problems.” In 2018 IEEE Congress on Evolutionary Computation (CEC), pp. 1-8. IEEE.
Crows: Askarzadeh A (2016). “A novel metaheuristic method for solving constrained engineering optimization problems: Crow search algorithm.” Computers & Structures, 169, pp. 1-12. doi: 10.1016/j.compstruc.2016.03.001
Crystal Energy: Feng X, Ma M and Yu H (2014). “Crystal Energy Optimization Algorithm.” Computational Intelligence, 32(2), pp. 284-322. doi: 10.1111/coin.12053
Cuckoos: Yang X and 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

Deer: Scottish Red Deer: Fard AF and 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), pp. 33-34.
Dendritic Cells: Greensmith J, Aickelin U and Cayzer S (2005). “Introducing dendritic cells as a novel immune-inspired algorithm for anomaly detection.” In International Conference on Artificial Immune Systems, pp. 153-167. Springer.
Dogs: Subramanian C, Sekar A and Subramanian K (2013). “A New Engineering Optimization Method: African Wild Dog Algorithm.” International Journal of Soft Computing, 8(3).
Dolphins: Dolphin Echolocation: Kaveh A and Farhoudi N (2013). “A new optimization method: Dolphin echolocation.” Advances in Engineering Software, 59, pp. 53-70. doi: 10.1016/j.advengsoft.2013.03.004
Dolphins: Dolphin Partners: Shiqin Y, Jianjun J and Guangxing Y (2009). “A Dolphin Partner Optimization.” In 2009 WRI Global Congress on Intelligent Systems. doi: 10.1109/gcis.2009.464
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), pp. 1053-1073. doi: 10.1007/s00521-015-1920-1
Duelists: Biyanto TR, Fibrianto HY, Nugroho G, Hatta AM, Listijorini E, Budiati T and Huda H (2016). “Duelist Algorithm: An Algorithm Inspired by How Duelist Improve Their Capabilities in a Duel.” In Tan Y, Shi Y and Niu B (eds.), Advances in Swarm Intelligence, pp. 39-47. ISBN 978-3-319-41000-5.

E

Eagles: Yang X and Deb S (2010). “Eagle Strategy Using Lévy Walk and Firefly Algorithms for Stochastic Optimization.” In Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), pp. 101-111. Springer Berlin Heidelberg. doi: 10.1007/978-3-642-12538-6_9
Earthworms: Wang G, Deb S and Coelho LDS (2015). “Earthworm optimization algorithm: a bio-inspired metaheuristic algorithm for global optimization problems.” International Journal of Bio-Inspired Computation, 7, pp. 1-23.
Ecogeography: Zheng Y, Ling H and Xue J (2014). “Ecogeography-based optimization: Enhancing biogeography-based optimization with ecogeographic barriers and differentiations.” Computers & Operations Research, 50, pp. 115-127. doi: 10.1016/j.cor.2014.04.013
Ecology: Parpinelli RS and 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 and Sossa H (2012). “Circle detection using electro-magnetism optimization.” Information Sciences, 182(1), pp. 40-55. doi: 10.1016/j.ins.2010.12.024
Elephants: Elephant Herds: Wang G, Deb S and dos S. Coelho L (2015). “Elephant Herding Optimization.” In 2015 3rd International Symposium on Computational and Business Intelligence (ISCBI). doi: 10.1109/iscbi.2015.8
Elephants: Regular Elephants: Deb S, Fong S and Tian Z (2015). “Elephant Search Algorithm for optimization problems.” In 2015 Tenth International Conference on Digital Information Management (ICDIM). doi: 10.1109/icdim.2015.7381893
Emotions: Xu Y, Cui Z and Zeng J (2010). “Social Emotional Optimization Algorithm for Nonlinear Constrained Optimization Problems.” In Swarm, Evolutionary, and Memetic Computing, pp. 583-590. Springer Berlin Heidelberg. doi: 10.1007/978-3-642-17563-3_68
Epidemics: Huang G (2016). “Artificial infectious disease optimization: A SEIQR epidemic dynamic model-based function optimization~algorithm.” Swarm and Evolutionary Computation, 27, pp. 31-67. doi: 10.1016/j.swevo.2015.09.007
Experts: Melo VVD (2014). “Kaizen programming.” In Proceedings of the 2014 conference on Genetic and evolutionary computation - GECCO \textquotesingle14. doi: 10.1145/2576768.2598264

F

FIFA World Cup: Razmjooy N, Khalilpour M and Ramezani M (2016). “A New Meta-Heuristic Optimization Algorithm Inspired by FIFA World Cup Competitions: Theory and Its Application in PID Designing for AVR System.” Journal of Control, Automation and Electrical Systems, 27(4), pp. 419-440. doi: 10.1007/s40313-016-0242-6
Fireflies: Yang X (2009). “Firefly Algorithms for Multimodal Optimization.” In Stochastic Algorithms: Foundations and Applications, pp. 169-178. Springer Berlin Heidelberg. doi: 10.1007/978-3-642-04944-6_14
Fireworks: Tan Y and Zhu Y (2010). “Fireworks Algorithm for Optimization.” In Lecture Notes in Computer Science, pp. 355-364. Springer Berlin Heidelberg. doi: 10.1007/978-3-642-13495-1_44
Fish: Catfish: Chuang L, Tsai S and Yang C (2011). “Improved binary particle swarm optimization using catfish effect for feature selection.” Expert Systems with Applications, 38(10), pp. 12699-12707. doi: 10.1016/j.eswa.2011.04.057
Fish: Cuttlefish: Eesa A, Abdulazeez A and Orman Z (2013). “Cuttlefish Algorithm - A Novel Bio-Inspired Optimization Algorithm.” International Journal of Scientific and Engineering Research, 4(9), pp. 1978-1986.
Fish: Fish Schools: Filho CJAB, de Lima Neto FB, Lins AJCC, Nascimento AIS and Lima MP (2008). “A novel search algorithm based on fish school behavior.” In 2008 IEEE International Conference on Systems, Man and Cybernetics. doi: 10.1109/icsmc.2008.4811695
Fish: Fish Swarms: Li X and Qian J (2003). “Studies on Artificial Fish Swarm Optimization Algorithm Based on Decomposition and Coordination Techniques.” J Circuits Systems, 1, pp. 1-6.
Flower Pollination: Yang X (2012). “Flower Pollination Algorithm for Global Optimization.” In Unconventional Computation and Natural Computation, pp. 240-249. Springer Berlin Heidelberg. doi: 10.1007/978-3-642-32894-7_27
Forests: Forest Regeneration: Moez H, Kaveh A and Taghizadieh N (2016). “Natural Forest Regeneration Algorithm: A New Meta-Heuristic.” Iranian Journal of Science and Technology, Transactions of Civil Engineering, 40(4), pp. 311-326. doi: 10.1007/s40996-016-0042-z
Forests: Tree Survival: Ghaemi M and Feizi-Derakhshi M (2014). “Forest Optimization Algorithm.” Expert Systems with Applications, 41(15), pp. 6676-6687. doi: 10.1016/j.eswa.2014.05.009
Fractals: Salimi H (2015). “Stochastic Fractal Search: A powerful metaheuristic algorithm.” Knowledge-Based Systems, 75, pp. 1-18. doi: 10.1016/j.knosys.2014.07.025
Frogs: Japanese Tree Frogs: Hernández H and Blum C (2012). “Distributed graph coloring: an approach based on the calling behavior of Japanese tree frogs.” Swarm Intelligence, 6(2), pp. 117-150. doi: 10.1007/s11721-012-0067-2
Frogs: Leaping: Eusuff MM and Lansey KE (2003). “Optimization of Water Distribution Network Design Using the Shuffled Frog Leaping Algorithm.” Journal of Water Resources Planning and Management, 129(3), pp. 210-225. doi: 10.1061/(asce)0733-9496(2003)129:3(210)
Fruit Fly: Pan W (2012). “A new Fruit Fly Optimization Algorithm: Taking the financial distress model as an example.” Knowledge-Based Systems, 26, pp. 69-74. doi: 10.1016/j.knosys.2011.07.001

G

Galaxies: Hosseini HS (2011). “Principal components analysis by the galaxy-based search algorithm: a novel metaheuristic for continuous optimisation.” International Journal of Computational Science and Engineering, 6(1/2), pp. 132. doi: 10.1504/ijcse.2011.041221
Gas Molecules: Brownian Motion: Abdechiri M, Meybodi MR and Bahrami H (2013). “Gases Brownian Motion Optimization: an Algorithm for Optimization (GBMO).” Applied Soft Computing, 13(5), pp. 2932-2946. doi: 10.1016/j.asoc.2012.03.068
Gas Molecules: Kinetic Energy: Moein S and Logeswaran R (2014). “KGMO: A swarm optimization algorithm based on the kinetic energy of gas molecules.” Information Sciences, 275, pp. 127-144. doi: 10.1016/j.ins.2014.02.026
Gene Expression: Ferreira C (2002). “Gene Expression Programming in Problem Solving.” In Soft Computing and Industry, pp. 635-653. Springer London. doi: 10.1007/978-1-4471-0123-9_54
General Relativity: Beiranvand H and Rokrok E (2015). “General Relativity Search Algorithm: A Global Optimization Approach.” International Journal of Computational Intelligence and Applications, 14(03), pp. 1550017. doi: 10.1142/s1469026815500170
Genes: Holland J (1975). Adaptation in Natural and Artificial Systems, An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. MIT Press.
Glow Worms: Krishnanand KN and Ghose D (2008). “Glowworm swarm optimization for simultaneous capture of multiple local optima of multimodal functions.” Swarm Intelligence, 3(2), pp. 87-124. doi: 10.1007/s11721-008-0021-5
Grasshoppers: Saremi S, Mirjalili S and Lewis A (2017). “Grasshopper Optimisation Algorithm: Theory and application.” Advances in Engineering Software, 105, pp. 30-47. doi: 10.1016/j.advengsoft.2017.01.004
Gravitation: Rashedi E, Nezamabadi-pour H and Saryazdi S (2009). “GSA: A Gravitational Search Algorithm.” Information Sciences, 179(13), pp. 2232-2248. doi: 10.1016/j.ins.2009.03.004
Great Deluge: Dueck G (1993). “New Optimization Heuristics: The Great Deluge and Record to Record Travel.” Journal of Computational Physics, 104(1), pp. 86-92. doi: 10.1006/jcph.1993.1010
Grenades: Ahrari A and Atai AA (2010). “Grenade Explosion Method\textemdashA novel tool for optimization of multimodal functions.” Applied Soft Computing, 10(4), pp. 1132-1140. doi: 10.1016/j.asoc.2009.11.032
Group Counselling: Eita MA and Fahmy MM (2009). “Group Counseling Optimization: A Novel Approach.” In Research and Development in Intelligent Systems XXVI, pp. 195-208. Springer London. doi: 10.1007/978-1-84882-983-1_14
Group Decision-Making: Zhang Q, Wang R, Yang J, Ding K, Li Y and Hu J (2017). “Collective decision optimization algorithm: A new heuristic optimization method.” Neurocomputing, 221, pp. 123-137. doi: 10.1016/j.neucom.2016.09.068

H

Hawks: Harris's Hawk: DeBruyne AS and Kaur BD (2016). “Harris's Hawk Multi-Objective Optimizer for Reference Point Problems.” In Proceedings on the International Conference on Artificial Intelligence (ICAI), pp. 287-292.
Heart: Hatamlou A (2014). “Heart: a novel optimization algorithm for cluster analysis.” Progress in Artificial Intelligence, 2(2-3), pp. 167-173. doi: 10.1007/s13748-014-0046-5
Hoopoe: El-Dosuky M, El-Bassiouny A, Hamza T and Rashad M (2012). “New Hoopoe Heuristic Optimization.” International Journal of Science and Advanced Technology, 2(9), pp. 85-90.
Hyenas: Dhiman G and Kumar V (2017). “Spotted hyena optimizer: A novel bio-inspired based metaheuristic technique for engineering applications.” Advances in Engineering Software, 114, pp. 48-70. doi: 10.1016/j.advengsoft.2017.05.014

I

Interior Design: Gandomi AH (2014). “Interior search algorithm (ISA): A novel approach for global optimization.” ISA Transactions, 53(4), pp. 1168-1183. doi: 10.1016/j.isatra.2014.03.018
Invasive Weeds: Mehrabian A and Lucas C (2006). “A novel numerical optimization algorithm inspired from weed colonization.” Ecological Informatics, 1(4), pp. 355-366. doi: 10.1016/j.ecoinf.2006.07.003
Ions: Javidy B, Hatamlou A and Mirjalili S (2015). “Ions motion algorithm for solving optimization problems.” Applied Soft Computing, 32, pp. 72-79. doi: 10.1016/j.asoc.2015.03.035

J

Jaguars: Chen C, Tsai Y, Liu I, Lai C, Yeh Y, Kuo S and Chou Y (2015). “A Novel Metaheuristic: Jaguar Algorithm with Learning Behavior.” In 2015 IEEE International Conference on Systems, Man, and Cybernetics. doi: 10.1109/smc.2015.282

K

Keshtel Duck: Hajiaghaei-Keshteli M and Aminnayeri M (2014). “Solving the integrated scheduling of production and rail transportation problem by Keshtel algorithm.” Applied Soft Computing, 25, pp. 184-203. doi: 10.1016/j.asoc.2014.09.034
Kidneys: Jaddi NS, Alvankarian J and Abdullah S (2017). “Kidney-inspired algorithm for optimization problems.” Communications in Nonlinear Science and Numerical Simulation, 42, pp. 358-369. doi: 10.1016/j.cnsns.2016.06.006
Krill: Gandomi AH and Alavi AH (2012). “Krill herd: A new bio-inspired optimization algorithm.” Communications in Nonlinear Science and Numerical Simulation, 17(12), pp. 4831-4845. doi: 10.1016/j.cnsns.2012.05.010

L

Ladybirds: Wang P, Zhu Z and Huang S (2013). “Seven-Spot Ladybird Optimization: A Novel and Efficient Metaheuristic Algorithm for Numerical Optimization.” The Scientific World Journal, 2013, pp. 1-11. doi: 10.1155/2013/378515
Lightning: Shareef H, Ibrahim AA and Mutlag AH (2015). “Lightning search algorithm.” Applied Soft Computing, 36, pp. 315-333. doi: 10.1016/j.asoc.2015.07.028
Lions: Wang B, Jin X and Cheng B (2012). “Lion pride optimizer: An optimization algorithm inspired by lion pride behavior.” Science China Information Sciences, 55(10), pp. 2369-2389. doi: 10.1007/s11432-012-4548-0
Locusts: Chen S (2009). “An Analysis of Locust Swarms on Large Scale Global Optimization Problems.” In Artificial Life: Borrowing from Biology, pp. 211-220. Springer Berlin Heidelberg. doi: 10.1007/978-3-642-10427-5_21

M

Markets: Ghorbani N and Babaei E (2014). “Exchange market algorithm.” Applied Soft Computing, 19, pp. 177-187. doi: 10.1016/j.asoc.2014.02.006
Meerkats: Klein CE and dos Santos Coelho L (2018). “Meerkats-inspired Algorithm for Global Optimization Problems.” In Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning.
Mine Explosions: Sadollah A, Bahreininejad A, Eskandar H and Hamdi M (2012). “Mine blast algorithm for optimization of truss structures with discrete variables.” Computers & Structures, 102-103, pp. 49-63. doi: 10.1016/j.compstruc.2012.03.013
Monkeys: Monkey Foraging: Mucherino A, Seref O, Seref O, Kundakcioglu OE and Pardalos P (2007). “Monkey search: a novel metaheuristic search for global optimization.” In AIP Conference Proceedings. doi: 10.1063/1.2817338
Monkeys: Spider Monkeys: Bansal JC, Sharma H, Jadon SS and Clerc M (2014). “Spider Monkey Optimization algorithm for numerical optimization.” Memetic Computing, 6(1), pp. 31-47. doi: 10.1007/s12293-013-0128-0
Mosquitos: Egg-laying Behavior: ul Amir Afsar Minhas F and Arif M (2011). “MOX: A novel global optimization algorithm inspired from Oviposition site selection and egg hatching inhibition in mosquitoes.” Applied Soft Computing, 11(8), pp. 4614-4625. doi: 10.1016/j.asoc.2011.07.020
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N

Neurons: Asil Gharebaghi S and Ardalan Asl M (2017). “NEW META-HEURISTIC OPTIMIZATION ALGORITHM USING NEURONAL COMMUNICATION.” _ International Journal of Optimization in Civil Engineering_, 7(3). http://ijoce.iust.ac.ir/article-1-306-en.pdf, <URL: http://ijoce.iust.ac.ir/article-1-306-en.html>.
Newton's Cooling Law: Kaveh A and Dadras A (2017). “A novel meta-heuristic optimization algorithm: Thermal exchange optimization.” Advances in Engineering Software, 110, pp. 69-84. doi: 10.1016/j.advengsoft.2017.03.014

O

Optics: Kashan AH (2015). “A new metaheuristic for optimization: Optics inspired optimization (OIO).” Computers & Operations Research, 55, pp. 99-125. doi: 10.1016/j.cor.2014.10.011

P

Paddy Fields: Premaratne U, Samarabandu J and Sidhu T (2009). “A new biologically inspired optimization algorithm.” In 2009 International Conference on Industrial and Information Systems (ICIIS). doi: 10.1109/iciinfs.2009.5429852
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Q

Quantum Superposition: Saire JEC and Tupac VYJ (2015). “An approach to real-coded quantum inspired evolutionary algorithm using particles filter.” In 2015 Latin America Congress on Computational Intelligence (LA-CCI). doi: 10.1109/la-cci.2015.7435984

R

Ravens: Torabi S and Safi-Esfahani F (2017). “Improved Raven Roosting Optimization algorithm (IRRO).” Swarm and Evolutionary Computation. doi: 10.1016/j.swevo.2017.11.006
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S

Salmon Migrations: Mozaffari A, Fathi A and Behzadipour S (2012). “The great salmon run: a novel bio-inspired algorithm for artificial system design and optimisation.” International Journal of Bio-Inspired Computation, 4(5), pp. 286-301.
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Sheep Flocks: Kim H and Ahn B (2001). “A new evolutionary algorithm based on sheep flocks heredity model.” In 2001 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (IEEE Cat. No.01CH37233). doi: 10.1109/pacrim.2001.953683
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Small World: Du H, Wu X and Zhuang J (2006). “Small-World Optimization Algorithm for Function Optimization.” In Lecture Notes in Computer Science, pp. 264-273. Springer Berlin Heidelberg. doi: 10.1007/11881223_33
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Soccer: Soccer Tournaments: Osaba E, Diaz F and Onieva E (2014). “Golden ball: a novel meta-heuristic to solve combinatorial optimization problems based on soccer concepts.” Applied Intelligence, 41(1), pp. 145-166. doi: 10.1007/s10489-013-0512-y
Social Behavior: Ray T and Liew K (2003). “Society and civilization: an optimization algorithm based on the simulation of social behavior.” IEEE Transactions on Evolutionary Computation, 7(4), pp. 386-396. doi: 10.1109/tevc.2003.814902
Social Behavior: Queuing: Zhang J, Xiao M, Gao L and Pan Q (2018). “Queuing search algorithm: A novel metaheuristic algorithm for solving engineering optimization problems.” Applied Mathematical Modelling, 63, pp. 464-490.
Social Engineering: Fard AMF and Hajiaghaei-Keshteli M (2017). “Social Engineering Optimization (SEO); A New Single-Solution Meta-heuristic Inspired by Social Engineering.” In International Conference on Industrial Engineering.
Social Spiders: Cuevas E, Cienfuegos M, Zald'\ivar D and Pérez-Cisneros M (2013). “A swarm optimization algorithm inspired in the behavior of the social-spider.” Expert Systems with Applications, 40(16), pp. 6374-6384. doi: 10.1016/j.eswa.2013.05.041
Sonar: Tzanetos A and Dounias G (2017). “A New Metaheuristic Method for Optimization: Sonar Inspired Optimization.” In Boracchi G, Iliadis L, Jayne C and Likas A (eds.), Engineering Applications of Neural Networks, pp. 417-428. ISBN 978-3-319-65172-9.
Sperm: Raouf OA and Hezam IM (2017). “Sperm motility algorithm: a novel metaheuristic approach for global optimisation.” International Journal of Operational Research, 28(2), pp. 143. doi: 10.1504/ijor.2017.10002079
Spirals: Tamura K and and Keiichiro Yasuda (2011). “Spiral Dynamics Inspired Optimization.” Journal of Advanced Computational Intelligence and Intelligent Informatics, 15(8), pp. 1116-1122. doi: 10.20965/jaciii.2011.p1116
Sports Championships: Kashan AH (2009). “League Championship Algorithm: A New Algorithm for Numerical Function Optimization.” In 2009 International Conference of Soft Computing and Pattern Recognition. doi: 10.1109/socpar.2009.21
Squirrels: Flying Squirrels: Jain M, Singh V and Rani A (2018). “A novel nature-inspired algorithm for optimization: Squirrel search algorithm.” Swarm and Evolutionary Computation. doi: 10.1016/j.swevo.2018.02.013
States of Matter: Cuevas E, Echavarr'\ia A and Ram'\irez-Ortegón MA (2013). “An optimization algorithm inspired by the States of Matter that improves the balance between exploration and exploitation.” Applied Intelligence, 40(2), pp. 256-272. doi: 10.1007/s10489-013-0458-0
States of Matter: Cuevas E, Reyna-Orta A and D'\iaz-Cortes M (2017). “A Multimodal Optimization Algorithm Inspired by the States of Matter.” Neural Processing Letters, 48(1), pp. 517-556. doi: 10.1007/s11063-017-9750-z
Swallows: Neshat M, Sepidnam G and Sargolzaei M (2012). “Swallow swarm optimization algorithm: a new method to optimization.” Neural Computing and Applications, 23(2), pp. 429-454. doi: 10.1007/s00521-012-0939-9
Symbiotic Organisms: Cheng M and Prayogo D (2014). “Symbiotic Organisms Search: A new metaheuristic optimization algorithm.” Computers & Structures, 139, pp. 98-112. doi: 10.1016/j.compstruc.2014.03.007

T

Teachers: Rao R, Savsani V and Vakharia D (2011). “Teaching\textendashlearning-based optimization: A novel method for constrained mechanical design optimization problems.” Computer-Aided Design, 43(3), pp. 303-315. doi: 10.1016/j.cad.2010.12.015
Termites: Hedayatzadeh R, Salmassi FA, Keshtgari M, Akbari R and Ziarati K (2010). “Termite colony optimization: A novel approach for optimizing continuous problems.” In 2010 18th Iranian Conference on Electrical Engineering. doi: 10.1109/iraniancee.2010.5507009
Troops of Soldiers: Chen T (2009). “A Simulative Bionic Intelligent Optimization Algorithm: Artificial Searching Swarm Algorithm and Its Performance Analysis.” In 2009 International Joint Conference on Computational Sciences and Optimization. doi: 10.1109/cso.2009.183
Tug of War: Kaveh A and Zolghadr A (2016). “A novel meta-heuristic algorithm: tug of war optimization.” Iran University of Science & Technology, 6(4), pp. 469-492.

U

V

Vaccination: Tayeb FB, Bessedik M, Benbouzid M, Cheurfi H and Blizak A (2017). “Research on Permutation Flow-shop Scheduling Problem based on Improved Genetic Immune Algorithm with vaccinated offspring.” Procedia Computer Science, 112, pp. 427-436. doi: 10.1016/j.procs.2017.08.055
Vehicles: Savsani P and Savsani V (2016). “Passing vehicle search (PVS): A novel metaheuristic algorithm.” Applied Mathematical Modelling, 40(5-6), pp. 3951-3978. doi: 10.1016/j.apm.2015.10.040
Vibrating Particles: Kaveh A and Ghazaan MI (2016). “Vibrating particles system algorithm for truss optimization with multiple natural frequency constraints.” Acta Mechanica, 228(1), pp. 307-322. doi: 10.1007/s00707-016-1725-z
Viruses: Virulence: Jaderyan M and Khotanlou H (2016). “Virulence Optimization Algorithm.” Applied Soft Computing, 43, pp. 596-618. doi: 10.1016/j.asoc.2016.02.038
Viruses: Virus Colonies: Li MD, Zhao H, Weng XW and Han T (2016). “A novel nature-inspired algorithm for optimization: Virus colony search.” Advances in Engineering Software, 92, pp. 65-88. doi: 10.1016/j.advengsoft.2015.11.004
Viruses: Virus Replication: Cortés P, Garc'\ia JM, Muñuzuri J and Onieva L (2008). “Viral systems: A new bio-inspired optimisation approach.” Computers & Operations Research, 35(9), pp. 2840-2860. doi: 10.1016/j.cor.2006.12.018
Virus: Swine Flu: Pattnaik S, Bakwad K, Sohi B, Ratho R and Devi S (2013). “Swine Influenza Models Based Optimization (SIMBO).” Applied Soft Computing, 13(1), pp. 628-653. doi: 10.1016/j.asoc.2012.07.010
Volleyball Leagues: Moghdani R and Salimifard K (2018). “Volleyball Premier League Algorithm.” Applied Soft Computing, 64, pp. 161-185. doi: 10.1016/j.asoc.2017.11.043
Vortices: Doğan B and Ölmez T (2015). “A new metaheuristic for numerical function optimization: Vortex Search algorithm.” Information Sciences, 293, pp. 125-145. doi: 10.1016/j.ins.2014.08.053
Vultures: Sur C, Sharma S and Shukla A (2013). “Egyptian Vulture Optimization Algorithm \textendash A New Nature Inspired Meta-heuristics for Knapsack Problem.” In The 9th International Conference on Computing and InformationTechnology (IC2IT2013), pp. 227-237. Springer Berlin Heidelberg. doi: 10.1007/978-3-642-37371-8_26

W

Wasps: Pinto P, Runkler TA and Sousa JM (2005). “Wasp swarm optimization of logistic systems.” In Adaptive and Natural Computing Algorithms, pp. 264-267. Springer.
Water: Hydrological Cycle: Wedyan A, Whalley J and Narayanan A (2017). “Hydrological Cycle Algorithm for Continuous Optimization Problems.” Journal of Optimization, 2017, pp. 1-25. doi: 10.1155/2017/3828420
Water: Intelligent Water Drops: Hosseini HS (2009). “The intelligent water drops algorithm: a nature-inspired swarm-based optimization algorithm.” International Journal of Bio-Inspired Computation, 1(1/2), pp. 71. doi: 10.1504/ijbic.2009.022775
Water: Rain: Kaboli SHA, Selvaraj J and Rahim N (2017). “Rain-fall optimization algorithm: A population based algorithm for solving constrained optimization problems.” Journal of Computational Science, 19, pp. 31-42. doi: 10.1016/j.jocs.2016.12.010
Water: Rain Drops: Jiang Q, Wang L, Hei X, Fei R, Yang D, Zou F, Li H, Cao Z and Lin Y (2014). “Optimal approximation of stable linear systems with a novel and efficient optimization algorithm.” In 2014 IEEE Congress on Evolutionary Computation (CEC). doi: 10.1109/cec.2014.6900366
Water: Water Cycle: Eskandar H, Sadollah A, Bahreininejad A and Hamdi M (2012). “Water cycle algorithm \textendash A novel metaheuristic optimization method for solving constrained engineering optimization problems.” Computers & Structures, 110-111, pp. 151-166. doi: 10.1016/j.compstruc.2012.07.010
Water: Water Evaporation: Kaveh A and Bakhshpoori T (2016). “Water Evaporation Optimization: A novel physically inspired optimization algorithm.” Computers & Structures, 167, pp. 69-85. doi: 10.1016/j.compstruc.2016.01.008
Water: Water Flow: Tran TH and Ng KM (2010). “A water-flow algorithm for flexible flow shop scheduling with~intermediate buffers.” Journal of Scheduling, 14(5), pp. 483-500. doi: 10.1007/s10951-010-0205-x
Water: Water Wave: Zheng Y (2015). “Water wave optimization: A new nature-inspired metaheuristic.” Computers & Operations Research, 55, pp. 1-11. doi: 10.1016/j.cor.2014.10.008
Whales: Binary Whales: K. SR, Panwar L, Panigrahi BK and Kumar R (2018). “Binary whale optimization algorithm: a new metaheuristic approach for profit-based unit commitment problems in competitive electricity markets.” Engineering Optimization, 51(3), pp. 369-389. doi: 10.1080/0305215x.2018.1463527
Whales: Killer Whales: Biyanto TR, Matradji, Irawan S, Febrianto HY, Afdanny N, Rahman AH, Gunawan KS, Pratama JA and Bethiana TN (2017). “Killer Whale Algorithm: An Algorithm Inspired by the Life of Killer Whale.” Procedia Computer Science, 124, pp. 151-157. doi: 10.1016/j.procs.2017.12.141
Whales: Regular Whales: Mirjalili S and Lewis A (2016). “The Whale Optimization Algorithm.” Advances in Engineering Software, 95, pp. 51-67. doi: 10.1016/j.advengsoft.2016.01.008
Whales: Sperm Whales: Ebrahimi A and Khamehchi E (2016). “Sperm whale algorithm: An effective metaheuristic algorithm for production optimization problems.” Journal of Natural Gas Science and Engineering, 29, pp. 211-222. doi: 10.1016/j.jngse.2016.01.001
Wind: Bayraktar Z, Komurcu M and 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
Wolves: Grey Wolves: Mirjalili S, Mirjalili SM and Lewis A (2014). “Grey Wolf Optimizer.” Advances in Engineering Software, 69, pp. 46-61. doi: 10.1016/j.advengsoft.2013.12.007
Wolves: Wolves: Tang R, Fong S, Yang X and 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, pp. 2499-2505.

X

Y

Yin-Yang Pairs: Punnathanam V and Kotecha P (2016). “Yin-Yang-pair Optimization: A novel lightweight optimization algorithm.” Engineering Applications of Artificial Intelligence, 54, pp. 62-79. doi: 10.1016/j.engappai.2016.04.004

Z

Zombies: Nguyen HT and Bhanu B (2012). “Zombie Survival Optimization: A swarm intelligence algorithm inspired by zombie foraging.” In Pattern Recognition (ICPR), 2012 21st International Conference on, pp. 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
André Maravilha - UFMG, Brazil
Carlos Fonseca - University of Coimbra, Portugal
Ciniro Nametala - UFMG, Brazil
Eduardo Hauck - UFJF, Brazil
Evan Cush
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 A. de Carvalho - UFMG, Brazil
Iztok Fister Jr. - University of Maribor, Slovenia
Jakub Grabski - Poznan University of Technology, Poland
James Brookhouse - University of Kent, England UK
jkpir
Kenneth Sörensen - University of Antwerp, Belgium
Lars Magnus Hvattum - Molde University College, Norway
Marc Sevaux - Université de Bretagne-Sud, France
Marco Mollinetti - University of Tsukuba, Japan
Marco Pranzo - Università di Siena, Italy
Marcus Ritt - UFRGS, Brazil
Nadarajen Veerapen - University of Stirling, Scotland UK
Robin Purshouse - University of Sheffield, England UK
Rubén Ruiz - Universitat Politècnica de València, Spain
Ruud Koot - Universiteit Utrecht, The Netherlands
Sara Silva - University of Lisbon
Sergio A. Rojas - Universidad Distrital de Bogotá, Colombia
Silvano Martello - University of Bologna
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

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/

fideoman / EC-Bestiary