Evolutionary Computation Bestiary

Updated 2019-11-26

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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.

Introduction

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

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

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

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

The Bestiary

A

African Buffalo : Odili JB, Kahar MNM (2016). “Solving the Traveling Salesman’s Problem Using the African Buffalo Optimization.” Computational Intelligence and Neuroscience, 2016, 1-12. doi: 10.1155/2016/1510256

: 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

: 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

: Wang H, Lu X, Zhang X, Wang Q, Deng Y (2014). “A Bio-Inspired Method for the Constrained Shortest Path Problem.” The Scientific World Journal, 2014, 1-11. doi: 10.1155/2014/271280 Amoeba: Plasmodium : Zhu L, Kim S, Hara M, Aono M (2018). “Remarkable problem-solving ability of unicellular amoeboid organism and its mechanism.” Royal Society Open Science, 5(12), 180396. doi: 10.1098/rsos.180396

: Zhu L, Kim S, Hara M, Aono M (2018). “Remarkable problem-solving ability of unicellular amoeboid organism and its mechanism.” Royal Society Open Science, 5(12), 180396. doi: 10.1098/rsos.180396 Anarchic Society : Shayeghi H, Dadashpour J (2012). “Anarchic Society Optimization Based PID Control of an Automatic Voltage Regulator (AVR) System.” Electrical and Electronic Engineering, 2(4), 199-207. doi: 10.5923/j.eee.20120204.05

: 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

: 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

: 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

: 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

: 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.

: 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

: 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

: 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

: 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

: 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

: 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

: 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 Barnacles Mating : Sulaiman MH, Mustaffa Z, Saari MM, Daniyal H, Mohamad AJ, Othman MR, Mohamed MR (2019). “Barnacles Mating Optimizer Algorithm for Optimization.” In Proceedings of the 10th National Technical Seminar on Underwater System Technology 2018, 211-218. Springer Singapore. doi: 10.1007/978-981-13-3708-6_18

: Sulaiman MH, Mustaffa Z, Saari MM, Daniyal H, Mohamad AJ, Othman MR, Mohamed MR (2019). “Barnacles Mating Optimizer Algorithm for Optimization.” In Proceedings of the 10th National Technical Seminar on Underwater System Technology 2018, 211-218. Springer Singapore. doi: 10.1007/978-981-13-3708-6_18 Bats : Yang X (2010). “A new metaheuristic bat-inspired algorithm.” In Nature inspired cooperative strategies for optimization (NICSO 2010), 65-74. Springer.

: 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

: 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

: 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

: 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

: 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

: 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

: 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

: 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

: 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

: 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 Bison : Kazikova A, Pluhacek M, Senkerik R, Viktorin A (2018). “Proposal of a New Swarm Optimization Method Inspired in Bison Behavior.” In Recent Advances in Soft Computing, 146-156. Springer International Publishing. doi: 10.1007/978-3-319-97888-8_13

: Kazikova A, Pluhacek M, Senkerik R, Viktorin A (2018). “Proposal of a New Swarm Optimization Method Inspired in Bison Behavior.” In Recent Advances in Soft Computing, 146-156. Springer International Publishing. doi: 10.1007/978-3-319-97888-8_13 Black Holes : Hatamlou A (2013). “Black hole: A new heuristic optimization approach for data clustering.” Information Sciences, 222, 175-184. doi: 10.1016/j.ins.2012.08.023

: 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

: 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

: 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 Buses : Bodaghi M, Samieefar K (2018). “Meta-heuristic bus transportation algorithm.” Iran Journal of Computer Science. doi: 10.1007/s42044-018-0025-2

: Bodaghi M, Samieefar K (2018). “Meta-heuristic bus transportation algorithm.” Iran Journal of Computer Science. doi: 10.1007/s42044-018-0025-2 Butterflies: Monarch Butterflies : Wang G, Deb S, Cui Z (2015). “Monarch butterfly optimization.” Neural Computing and Applications. doi: 10.1007/s00521-015-1923-y

: Wang G, Deb S, Cui Z (2015). “Monarch butterfly optimization.” Neural Computing and Applications. doi: 10.1007/s00521-015-1923-y Butterflies: Regular Butterflies : Arora S, Singh S (2018). “Butterfly optimization algorithm: a novel approach for global optimization.” Soft Computing. doi: 10.1007/s00500-018-3102-4

C

Camels : M. K. Ibrahim RSA (2016). “Novel Optimization Algorithm Inspired by Camel Traveling Behavior.” Iraq J. Electrical and Electronic Engineering, 12(2), 167-177. ISSN 18145892, <URL: https://www.iasj.net/iasj?func=article&aId=118375>.

: M. K. Ibrahim RSA (2016). “Novel Optimization Algorithm Inspired by Camel Traveling Behavior.” Iraq J. Electrical and Electronic Engineering, 12(2), 167-177. ISSN 18145892, <URL: https://www.iasj.net/iasj?func=article&aId=118375>. 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

: 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

: 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

: 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

: Kaveh A, Talatahari S (2010). “A novel heuristic optimization method: charged system search.” Acta Mechanica, 213(3-4), 267-289. doi: 10.1007/s00707-009-0270-4 Cheetah : Klein CE, Mariani V, dos Santos Coelho L (2018). “Cheetah Based Optimization Algorithm: A Novel Swarm Intelligence Paradigm.” In Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning.

: Klein CE, Mariani V, dos Santos Coelho L (2018). “Cheetah Based Optimization Algorithm: A Novel Swarm Intelligence Paradigm.” In Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Chemical Reactions : Alatas B (2011). “ACROA: Artificial Chemical Reaction Optimization Algorithm for global optimization.” Expert Systems with Applications, 38(10), 13170-13180. doi: 10.1016/j.eswa.2011.04.126

: 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

: 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

: 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

: 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

: 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

: 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

: 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

: 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

: 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 Coyotes : Pierezan J, Coelho LDS (2018). “Coyote Optimization Algorithm: A New Metaheuristic for Global Optimization Problems.” In 2018 IEEE Congress on Evolutionary Computation (CEC), 1-8. IEEE.

: Pierezan J, Coelho LDS (2018). “Coyote Optimization Algorithm: A New Metaheuristic for Global Optimization Problems.” In 2018 IEEE Congress on Evolutionary Computation (CEC), 1-8. IEEE. 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

: 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

: 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\é$\mathsemicolon$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.

: Fard AF, Hajiaghaei-Keshteli M (2016). “Red Deer Algorithm (RDA); A New Optimization Algorithm Inspired by Red Deers’ Mating.” In International Conference on Industrial Engineering, IEEE.,(2016 e), 33-34. Dendritic Cells : Greensmith J, Aickelin U, Cayzer S (2005). “Introducing dendritic cells as a novel immune-inspired algorithm for anomaly detection.” In International Conference on Artificial Immune Systems, 153-167. Springer.

: Greensmith J, Aickelin U, Cayzer S (2005). “Introducing dendritic cells as a novel immune-inspired algorithm for anomaly detection.” In International Conference on Artificial Immune Systems, 153-167. Springer. Dogs : Subramanian C, Sekar A, Subramanian K (2013). “A New Engineering Optimization Method: African Wild Dog Algorithm.” International Journal of Soft Computing, 8(3).

: 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

: 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

: 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

: 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

: Yang X, Deb S (2010). “Eagle Strategy Using Lévy Walk and Firefly Algorithms for Stochastic Optimization.” In Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), 101-111. Springer Berlin Heidelberg. doi: 10.1007/978-3-642-12538-6_9 Earthworms : Wang G, Deb S, Coelho LDS (2015). “Earthworm optimization algorithm: a bio-inspired metaheuristic algorithm for global optimization problems.” International Journal of Bio-Inspired Computation, 7, 1-23.

: Wang G, Deb S, Coelho LDS (2015). “Earthworm optimization algorithm: a bio-inspired metaheuristic algorithm for global optimization problems.” International Journal of Bio-Inspired Computation, 7, 1-23. 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

: 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

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: Ebrahimi A, Khamehchi E (2016). “Sperm whale algorithm: An effective metaheuristic algorithm for production optimization problems.” Journal of Natural Gas Science and Engineering, 29, 211-222. doi: 10.1016/j.jngse.2016.01.001 Wind : Bayraktar Z, Komurcu M, Werner DH (2010). “Wind Driven Optimization (WDO): A novel nature-inspired optimization algorithm and its application to electromagnetics.” In 2010 IEEE Antennas and Propagation Society International Symposium. doi: 10.1109/aps.2010.5562213

: Bayraktar Z, Komurcu M, Werner DH (2010). “Wind Driven Optimization (WDO): A novel nature-inspired optimization algorithm and its application to electromagnetics.” In 2010 IEEE Antennas and Propagation Society International Symposium. doi: 10.1109/aps.2010.5562213 Wolves: Grey Wolves : Mirjalili S, Mirjalili SM, Lewis A (2014). “Grey Wolf Optimizer.” Advances in Engineering Software, 69, 46-61. doi: 10.1016/j.advengsoft.2013.12.007

: Mirjalili S, Mirjalili SM, Lewis A (2014). “Grey Wolf Optimizer.” Advances in Engineering Software, 69, 46-61. doi: 10.1016/j.advengsoft.2013.12.007 Wolves: Wolves : Tang R, Fong S, Yang X, Deb S (2012). “Wolf search algorithm with ephemeral memory.” In Seventh International Conference on Digital Information Management (ICDIM 2012). doi: 10.1109/icdim.2012.6360147

: Tang R, Fong S, Yang X, Deb S (2012). “Wolf search algorithm with ephemeral memory.” In Seventh International Conference on Digital Information Management (ICDIM 2012). doi: 10.1109/icdim.2012.6360147 Worms : Arnaout J (2014). “Worm optimization: a novel optimization algorithm inspired by C. Elegans.” In Proceedings of the 2014 International Conference on Industrial Engineering and Operations Management, 2499-2505.

X

Y

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.

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

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

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/