Evolutionary Computation Bestiary

Updated 2018-07-12

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"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.

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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!

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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
• 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
• 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
• 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
• 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.

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
• Bats: Yang X (2010). “A new metaheuristic bat-inspired algorithm.” In Nature inspired cooperative strategies for optimization (NICSO 2010), 65-74. Springer.
• 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: 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: Kallioras NA, Lagaros ND, Avtzis DN (2018). “Pity beetle algorithm \textendash 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
• Big Bang: Erol OK, Eksin I (2006). “A new optimization method: Big Bang\textendashBig 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
• 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
• Blind Naked Mole Rats: Taherdangkoo M, Shirzadi MH, Yazdi M, Bagheri MH (2013). “A robust clustering method based on blind, naked mole-rats (BNMR) algorithm.” Swarm and Evolutionary Computation, 10, 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), 35-62. doi: 10.4018/ijsir.2011100103
• Butterflies: Monarch Butterflies: Wang G, Deb S, Cui Z (2015). “Monarch butterfly optimization.” Neural Computing and Applications. doi: 10.1007/s00521-015-1923-y

C

• Camels: M. K. Ibrahim RSA (2016). “Novel Optimization Algorithm Inspired by Camel Traveling Behavior.” Iraq J. Electrical and Electronic Engineering, 12(2). doi: 10.1007/s00707-009-0270-4
• 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: 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
• 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
• 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
• 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
• 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
• 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
• 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

• 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.
• Dogs: Subramanian C, Sekar A, Subramanian K (2013). “A New Engineering Optimization Method: African Wild Dog Algorithm.” International Journal of Soft Computing, 8(3).
• 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
• 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.

E

• Eagles: 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
• 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
• Elephants: Elephant Herds: Wang G, Deb S, 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, 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, Zeng J (2010). “Social Emotional Optimization Algorithm for Nonlinear Constrained Optimization Problems.” In Swarm, Evolutionary, and Memetic Computing, 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, 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, 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), 419-440. doi: 10.1007/s40313-016-0242-6
• Fireflies: Yang X (2009). “Firefly Algorithms for Multimodal Optimization.” In Stochastic Algorithms: Foundations and Applications, 169-178. Springer Berlin Heidelberg. doi: 10.1007/978-3-642-04944-6_14
• Fireworks: Tan Y, Zhu Y (2010). “Fireworks Algorithm for Optimization.” In Lecture Notes in Computer Science, 355-364. Springer Berlin Heidelberg. doi: 10.1007/978-3-642-13495-1_44
• Fish: Catfish: Chuang L, Tsai S, Yang C (2011). “Improved binary particle swarm optimization using catfish effect for feature selection.” Expert Systems with Applications, 38(10), 12699-12707. doi: 10.1016/j.eswa.2011.04.057
• Fish: Cuttlefish: Eesa A, Abdulazeez A, Orman Z (2013). “Cuttlefish Algorithm - A Novel Bio-Inspired Optimization Algorithm.” International Journal of Scientific and Engineering Research, 4(9), 1978-1986.
• Fish: Fish Schools: Filho CJAB, de Lima Neto FB, Lins AJCC, Nascimento AIS, 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, Qian J (2003). “Studies on Artificial Fish Swarm Optimization Algorithm Based on Decomposition and Coordination Techniques.” J Circuits Systems, 1, 1-6.
• Flower Pollination: Yang X (2012). “Flower Pollination Algorithm for Global Optimization.” In Unconventional Computation and Natural Computation, 240-249. Springer Berlin Heidelberg. doi: 10.1007/978-3-642-32894-7_27
• Forests: Forest Regeneration: Moez H, Kaveh A, Taghizadieh N (2016). “Natural Forest Regeneration Algorithm: A New Meta-Heuristic.” Iranian Journal of Science and Technology, Transactions of Civil Engineering, 40(4), 311-326. doi: 10.1007/s40996-016-0042-z
• Forests: Tree Survival: Ghaemi M, Feizi-Derakhshi M (2014). “Forest Optimization Algorithm.” Expert Systems with Applications, 41(15), 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, 1-18. doi: 10.1016/j.knosys.2014.07.025
• Frogs: Japanese Tree Frogs: Hernández H, Blum C (2012). “Distributed graph coloring: an approach based on the calling behavior of Japanese tree frogs.” Swarm Intelligence, 6(2), 117-150. doi: 10.1007/s11721-012-0067-2
• Frogs: Leaping: Eusuff MM, Lansey KE (2003). “Optimization of Water Distribution Network Design Using the Shuffled Frog Leaping Algorithm.” Journal of Water Resources Planning and Management, 129(3), 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, 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), 132. doi: 10.1504/ijcse.2011.041221
• Gas Molecules: Brownian Motion: Abdechiri M, Meybodi MR, Bahrami H (2013). “Gases Brownian Motion Optimization: an Algorithm for Optimization (GBMO).” Applied Soft Computing, 13(5), 2932-2946. doi: 10.1016/j.asoc.2012.03.068
• Gas Molecules: Kinetic Energy: Moein S, Logeswaran R (2014). “KGMO: A swarm optimization algorithm based on the kinetic energy of gas molecules.” Information Sciences, 275, 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, 635-653. Springer London. doi: 10.1007/978-1-4471-0123-9_54
• General Relativity: Beiranvand H, Rokrok E (2015). “General Relativity Search Algorithm: A Global Optimization Approach.” International Journal of Computational Intelligence and Applications, 14(03), 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, Ghose D (2008). “Glowworm swarm optimization for simultaneous capture of multiple local optima of multimodal functions.” Swarm Intelligence, 3(2), 87-124. doi: 10.1007/s11721-008-0021-5
• Grasshoppers: Saremi S, Mirjalili S, Lewis A (2017). “Grasshopper Optimisation Algorithm: Theory and application.” Advances in Engineering Software, 105, 30-47. doi: 10.1016/j.advengsoft.2017.01.004
• Gravitation: Rashedi E, Nezamabadi-pour H, Saryazdi S (2009). “GSA: A Gravitational Search Algorithm.” Information Sciences, 179(13), 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), 86-92. doi: 10.1006/jcph.1993.1010
• Grenades: Ahrari A, Atai AA (2010). “Grenade Explosion Method—A novel tool for optimization of multimodal functions.” Applied Soft Computing, 10(4), 1132-1140. doi: 10.1016/j.asoc.2009.11.032
• Group Counselling: Eita MA, Fahmy MM (2009). “Group Counseling Optimization: A Novel Approach.” In Research and Development in Intelligent Systems XXVI, 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, Hu J (2017). “Collective decision optimization algorithm: A new heuristic optimization method.” Neurocomputing, 221, 123-137. doi: 10.1016/j.neucom.2016.09.068

H

• Heart: Hatamlou A (2014). “Heart: a novel optimization algorithm for cluster analysis.” Progress in Artificial Intelligence, 2(2-3), 167-173. doi: 10.1007/s13748-014-0046-5
• Hoopoe: El-Dosuky M, El-Bassiouny A, Hamza T, Rashad M (2012). “New Hoopoe Heuristic Optimization.” International Journal of Science and Advanced Technology, 2(9), 85-90.
• Hyenas: Dhiman G, Kumar V (2017). “Spotted hyena optimizer: A novel bio-inspired based metaheuristic technique for engineering applications.” Advances in Engineering Software, 114, 48-70. 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), 1168-1183. doi: 10.1016/j.isatra.2014.03.018
• Invasive Weeds: Mehrabian A, Lucas C (2006). “A novel numerical optimization algorithm inspired from weed colonization.” Ecological Informatics, 1(4), 355-366. doi: 10.1016/j.ecoinf.2006.07.003
• Ions: Javidy B, Hatamlou A, Mirjalili S (2015). “Ions motion algorithm for solving optimization problems.” Applied Soft Computing, 32, 72-79. doi: 10.1016/j.asoc.2015.03.035

J

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

L

• Ladybirds: Wang P, Zhu Z, Huang S (2013). “Seven-Spot Ladybird Optimization: A Novel and Efficient Metaheuristic Algorithm for Numerical Optimization.” The Scientific World Journal, 2013, 1-11. doi: 10.1155/2013/378515
• Lightning: Shareef H, Ibrahim AA, Mutlag AH (2015). “Lightning search algorithm.” Applied Soft Computing, 36, 315-333. doi: 10.1016/j.asoc.2015.07.028
• Lions: Wang B, Jin X, Cheng B (2012). “Lion pride optimizer: An optimization algorithm inspired by lion pride behavior.” Science China Information Sciences, 55(10), 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, 211-220. Springer Berlin Heidelberg. doi: 10.1007/978-3-642-10427-5_21

M

• Markets: Ghorbani N, Babaei E (2014). “Exchange market algorithm.” Applied Soft Computing, 19, 177-187. doi: 10.1016/j.asoc.2014.02.006
• Mine Explosions: Sadollah A, Bahreininejad A, Eskandar H, Hamdi M (2012). “Mine blast algorithm for optimization of truss structures with discrete variables.” Computers & Structures, 102-103, 49-63. doi: 10.1016/j.compstruc.2012.03.013
• Monkeys: Monkey Foraging: Mucherino A, Seref O, Seref O, Kundakcioglu OE, 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, Clerc M (2014). “Spider Monkey Optimization algorithm for numerical optimization.” Memetic Computing, 6(1), 31-47. doi: 10.1007/s12293-013-0128-0
• Moths: Mirjalili S (2015). “Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm.” Knowledge-Based Systems, 89, 228-249. doi: 10.1016/j.knosys.2015.07.006
• Mountain Climbers: Zhang LM, Dahlmann C, Zhang Y (2009). “Human-Inspired Algorithms for continuous function optimization.” In 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems. doi: 10.1109/icicisys.2009.5357838
• Multiverse: Mirjalili S, Mirjalili SM, Hatamlou A (2015). “Multi-Verse Optimizer: a nature-inspired algorithm for global optimization.” Neural Computing and Applications, 27(2), 495-513. doi: 10.1007/s00521-015-1870-7
• Mushroom Reproduction: Bidar M, Kanan HR, Mouhoub M, Sadaoui S (2018). “Mushroom Reproduction Optimization (MRO): A Novel Nature-Inspired Evolutionary Algorithm.” In 2018 IEEE Congress on Evolutionary Computation.
• Musicians: Geem ZW, Kim JH, Loganathan G (2001). “A New Heuristic Optimization Algorithm: Harmony Search.” SIMULATION, 76(2), 60-68. doi: 10.1177/003754970107600201

N

• Neurons: Asil Gharebaghi S, 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, Dadras A (2017). “A novel meta-heuristic optimization algorithm: Thermal exchange optimization.” Advances in Engineering Software, 110, 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, 99-125. doi: 10.1016/j.cor.2014.10.011

P

• Paddy Fields: Premaratne U, Samarabandu J, 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
• Parliamentarist Elections: Borji A (2007). “A New Global Optimization Algorithm Inspired by Parliamentary Political Competitions.” In MICAI 2007: Advances in Artificial Intelligence, 61-71. Springer Berlin Heidelberg. doi: 10.1007/978-3-540-76631-5_7
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Q

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R

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S

• Salmon Migrations: Mozaffari A, Fathi A, 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), 286-301.
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• Symbiotic Organisms: Cheng M, Prayogo D (2014). “Symbiotic Organisms Search: A new metaheuristic optimization algorithm.” Computers & Structures, 139, 98-112. doi: 10.1016/j.compstruc.2014.03.007

T

• Teachers: Rao R, Savsani V, Vakharia D (2011). “Teaching\textendashlearning-based optimization: A novel method for constrained mechanical design optimization problems.” Computer-Aided Design, 43(3), 303-315. doi: 10.1016/j.cad.2010.12.015
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U

V

• Vaccination: Tayeb FB, Bessedik M, Benbouzid M, Cheurfi H, Blizak A (2017). “Research on Permutation Flow-shop Scheduling Problem based on Improved Genetic Immune Algorithm with vaccinated offspring.” Procedia Computer Science, 112, 427-436. doi: 10.1016/j.procs.2017.08.055
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W

• Wasps: Pinto P, Runkler TA, Sousa JM (2005). “Wasp swarm optimization of logistic systems.” In Adaptive and Natural Computing Algorithms, 264-267. Springer.
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X

Y

• 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

• 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.

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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
• 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
• Iago A. de Carvalho - UFMG, Brazil
• Iztok Fister Jr. - University of Maribor, Slovenia
• Jakub Grabski - Poznan University of Technology, Poland
• 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
• James Brookhouse - University of Kent, England UK

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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:

1. the work must be in a peer reviewed publication (journal or conference);
2. 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/