https://github.com/1kastner/ec-bestiary

A bestiary of evolutionary, swarm and other metaphor-based algorithms

https://github.com/1kastner/ec-bestiary

Repository

A bestiary of evolutionary, swarm and other metaphor-based algorithms

https://github.com/1kastner/EC-Bestiary/blob/master/

# Evolutionary Computation Bestiary  
[![DOI](https://zenodo.org/badge/54759561.svg)](https://zenodo.org/badge/latestdoi/54759561)

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](https://en.wikipedia.org/wiki/Machado_de_Assis), *[The Psychiatrist](https://en.wikipedia.org/wiki/O_alienista)*.

***

## 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](https://en.wikipedia.org/wiki/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
![BioHeuristics GO](img/BioHeuristics%20GO.png)

### 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 ](https://doi.org/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 ](https://doi.org/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 ](https://doi.org/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 ](https://doi.org/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 ](https://doi.org/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 ](https://doi.org/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 ](https://doi.org/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 ](https://doi.org/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 ](https://doi.org/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 ](https://doi.org/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 ](https://doi.org/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 ](https://doi.org/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 ](https://doi.org/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 ](https://doi.org/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 ](https://doi.org/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 ](https://doi.org/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 ](https://doi.org/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 ](https://doi.org/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 ](https://doi.org/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 ](https://doi.org/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 ](https://doi.org/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 ](https://doi.org/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 ](https://doi.org/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 ](https://doi.org/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 ](https://doi.org/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 ](https://doi.org/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 ](https://doi.org/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 ](https://doi.org/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 ](https://doi.org/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 ](https://doi.org/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 ](https://doi.org/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, .
- **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 ](https://doi.org/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 ](https://doi.org/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 ](https://doi.org/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 ](https://doi.org/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 ](https://doi.org/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 ](https://doi.org/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 ](https://doi.org/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 ](https://doi.org/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 ](https://doi.org/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 ](https://doi.org/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. 157172. ISSN 0921-7126, doi: [10.3233/AIC-2012-0526 ](https://doi.org/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 ](https://doi.org/10.1145/1830483.1830526 )
- **Coral Reefs**: Salcedo-Sanz S, Ser JD, Landa-Torres I, Gil-Lpez 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 ](https://doi.org/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 ](https://doi.org/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 ](https://doi.org/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 ](https://doi.org/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 ](https://doi.org/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 ](https://doi.org/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 ](https://doi.org/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 Lvy 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 ](https://doi.org/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 ](https://doi.org/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 ](https://doi.org/10.1109/nabic.2011.6089631 )
- **Electromagnetism**: Cuevas E, Oliva D, Zaldivar D, Prez-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 ](https://doi.org/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 ](https://doi.org/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 ](https://doi.org/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 ](https://doi.org/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 ](https://doi.org/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 ](https://doi.org/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 ](https://doi.org/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 ](https://doi.org/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 ](https://doi.org/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 ](https://doi.org/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 ](https://doi.org/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 ](https://doi.org/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 ](https://doi.org/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 ](https://doi.org/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 ](https://doi.org/10.1016/j.knosys.2014.07.025 )
- **Frogs: Japanese Tree Frogs**: Hernndez 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 ](https://doi.org/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 ](https://doi.org/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 ](https://doi.org/10.1504/ijcse.2011.041221 )
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- **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 ](https://doi.org/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 ](https://doi.org/10.1016/j.advengsoft.2015.11.004 )
- **Viruses: Virus Replication**: Corts P, Garc\'\ia JM, Muuzuri 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 ](https://doi.org/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 ](https://doi.org/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 ](https://doi.org/10.1016/j.asoc.2017.11.043 )
- **Vortices**: Doan B and lmez 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 ](https://doi.org/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 ](https://doi.org/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 ](https://doi.org/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 ](https://doi.org/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 ](https://doi.org/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 ](https://doi.org/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 ](https://doi.org/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 ](https://doi.org/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 ](https://doi.org/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 ](https://doi.org/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 ](https://doi.org/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 ](https://doi.org/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 ](https://doi.org/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 ](https://doi.org/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 ](https://doi.org/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 ](https://doi.org/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 ](https://doi.org/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 ](https://doi.org/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](mailto:caranha@cs.tsukuba.ac.jp), Tsukuba University, Japan.  
- [Felipe Campelo](mailto:fcampelo@ufmg.br), 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!)

- Adr Steyn - University of Stellenbosch, South Africa
- Alberto Franzin - Universit Libre de Bruxelles, Belgium
- Andr 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 - Universit 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 Srensen - University of Antwerp, Belgium
- Lars Magnus Hvattum - Molde University College, Norway
- Marc Sevaux - Universit de Bretagne-Sud, France
- Marco Mollinetti - University of Tsukuba, Japan
- Marco Pranzo - Universit di Siena, Italy
- Marcus Ritt - UFRGS, Brazil
- Nadarajen Veerapen - University of Stirling, Scotland UK
- Robin Purshouse - University of Sheffield, England UK
- Rubn Ruiz - Universitat Politcnica de Valncia, Spain
- Ruud Koot - Universiteit Utrecht, The Netherlands
- Sara Silva - University of Lisbon
- Sergio A. Rojas - Universidad Distrital de Bogot, Colombia
- Silvano Martello - University of Bologna
- Stefan Vo - Universitt Hamburg, Germany
- Thomas Jacob Riis Stidsen - Danmarks Tekniske Universitet, Denmark
- Thomas Sttzle - Universit 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](mailto:caranha@cs.tsukuba.ac.jp) or [Felipe](mailto:fcampelo@ufmg.br), or report an issue on our [Github repo](https://github.com/fcampelo/EC-bestiary). 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](http://onlinelibrary.wiley.com/doi/10.1111/itor.12001/epdf)", by Kenneth Sresen  
- Need inspiration for your next Bioinspired algorithm? Check Marco Scirea and Julian Togelius' [Daily Bio-heuristics bot](https://twitter.com/BioHeuristics).  
- Some of the algorithms listed here were found in a list compiled by Iztok Fister Jr. _et al._, which is available [here](http://www.iztok-jr-fister.eu/static/publications/21.pdf). Iztok also recently published [this paper](http://www.iztok-jr-fister.eu/static/publications/Stu2016.pdf) reflecting on the proliferation of metaphors in EC research.  
- A fantastic parody of this whole metaphor craze can be read [here](http://www.oneweirdkerneltrick.com/spectral.pdf). 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/](http://creativecommons.org/licenses/by-nc-sa/4.0/)

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