EC Bestiary

Updated May 3rd, 2017

"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.


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

BioHeuristics GO


  • African buffalo - J.B. Odili, M.N. Mohmad Kahar. "Solving the Traveling Salesman's Problem Using the African Buffalo Optimization". Computational intelligence and neuroscience, vol. 2016, Article ID 1510256, 12 pages, 2016. [DOI] [Google Scholar]
  • Algae - S.A. Uymaz, G.Tezel, E.Yel, "Artificial Algae Algorithm (AAA) for Nonlinear Global Optimization" Applied Soft Computing 31, pp 153--171 (2015) [DOI]
  • Amoeba - Wang, H., Lu, X., Zhang, X., Wang, Q. and Deng, Y.. "A bio-inspired method for the constrained shortest path problem." The Scientific World Journal 2014 (2014). [DOI] [Google Scholar]
  • Anarchic societies - H. Shayeghi, J. Dadashpour. "Anarchic society optimization based pid control of an automatic voltage regulator (avr) system". Electrical and Electronic Engineering, 2(4), pp.199-207, 2012. [DOI] [Google Scholar]
  • Animal behavior
    • Hunting Naderi, etal. "Mathematical models and a hunting search algorithm for the no-wait flowshop scheduling with parallel machines", Int. J. Prod. Res., 52(9), 2014. [DOI]
    • Migration P. Civicioglu, “Transforming geocentric cartesian coordinates to geodetic coordinates by using differential search algorithm.” Computers & Geosciences, 46, 229–247, 2012. [DOI]
    • Searching S. He, Q.H. Wu, J.R. Saunders. "Group search optimizer: an optimization algorithm inspired by animal searching behavior." IEEE Transactions on evolutionary computation, 13(5), pp.973-990, 2009. [DOI] [Google Scholar]
  • Ant Colony - A. Colorni, M. Dorigo, V. Maniezzo, "Distributed Optimization by Ant Colonies", Proceedings of the European Conference on Artificial Life, pp.134-142, 1991.
  • Ant Lion - S. Mirjalili. "The ant lion optimizer". Advances in Engineering Software, 83, pp.80-98, 2015. [DOI] [Google Scholar]
  • Antibodies - L.N. de Castro, F.J. von Zuben. "The clonal selection algorithm with engineering applications." Proceedings of the Genetic and Evolutionary Computation Conference (GECCO'2000), pp. 36-39. 2000.[Google Scholar]


  • Bacteria
    • Bacterial Chemotaxis - SD Müller et al.. "Optimization based on bacterial chemotaxis." IEEE Transactions on Evolutionary Computation 6(1):16-29, 2002 [DOI] [Google Scholar]
    • Bacterial foraging - K.M. Passino. "Biomimicry of bacterial foraging for distributed optimization and control". IEEE control systems, 22(3), pp.52-67, 2002. [DOI] [Google Scholar]
    • Bacterial swarming - Y. Chu et al.. "A fast bacterial swarming algorithm for high-dimensional function optimization." In 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) (pp. 3135-3140). IEEE, June 2008. [DOI] [Google Scholar]
    • Magnetotactic bacteria - Hongwei Mo and Lifang Xu. "Magnetotactic bacteria optimization algorithm for multimodal optimization." In Swarm Intelligence (SIS), 2013 IEEE Symposium on, pp. 240-247. IEEE, 2013. [DOI] [Google Scholar]
  • Bats - Yang, Xin-She. "A new metaheuristic bat-inspired algorithm". Nature inspired cooperative strategies for optimization (NICSO 2010). Springer Berlin Heidelberg, 2010. 65-74. [Google Scholar]
  • Bees
    • Bee Colonies - Dusan Teodorovic et al. "Bee colony optimization: principles and applications." Neural Network Applications in Electrical Engineering, 2006. NEUREL 2006. 8th Seminar on. IEEE, 2006.[DOI] [Google Scholar]
    • Bumblebees - F. Comellas, J. Martinez-Navarro. "Bumblebees: a multiagent combinatorial optimization algorithm inspired by social insect behaviour." In Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation (pp. 811-814). ACM, June 2009. [DOI] [Google Scholar]
    • Honey-bee marriage - H.A. Abbass. "MBO: Marriage in honey bees optimization - A haplometrosis polygynous swarming approach." In Evolutionary Computation, 2001. Proceedings of the 2001 Congress on (Vol. 1, pp. 207-214). IEEE. [DOI] [Google Scholar]
    • Queen bees - S.H. Jung. "Queen-bee evolution for genetic algorithms." Electronics letters, 39(6), p.1, 2003. [DOI] [Google Scholar]
  • Big bang - Osman K. Erol and Ibrahim Eksin. "A new optimization method: big bang–big crunch." Advances in Engineering Software 37(2):106-111, 2006. [DOI] [Google Scholar]
  • Biogeography - D Simon. "Biogeography-based optimization." Evolutionary Computation, IEEE Transactions on, 2008. [DOI] [Google Scholar]
  • Birds
    • Mating - Alireza Askarzadeh. "Bird mating optimizer: an optimization algorithm inspired by bird mating strategies." Communications in Nonlinear Science and Numerical Simulation 19(4):1213-1228, 2014.[DOI] [Google Scholar]
    • Migrating - Duman et al. "Migrating Birds Optimization: A new metaheuristic approach and its performance on quadratic assignment problem", Information Sciences Volume 217, Pages 65–77, 2012. [DOI]
  • Black holes - Abdolreza Hatamlou. "Black hole: A new heuristic optimization approach for data clustering." Information Sciences 222:175-184, 2013. [DOI] [Google Scholar]
  • Blind, naked mole rats - Taherdangkoo, Mohammad, et al. "A robust clustering method based on blind, naked mole-rats (BNMR) algorithm." Swarm and Evolutionary Computation 10:1-11, 2013. [DOI] [Google Scholar]
  • Brainstorms - Y. Shi. "An optimization algorithm based on brainstorming process." Emerging Research on Swarm Intelligence and Algorithm Optimization, pp.1-35, 2015 [DOI] [Google Scholar]
  • Butterflies - G. Wang, S. Deb, Z. Cui. "Monarch butterfly optimization.", Neural Computing and Applications 1-20, 2015. [DOI]


  • Cats - Shu-Chuan Chu, Pei-Wei Tsai, and Jeng-Shyang Pan. "Cat swarm optimization." PRICAI 2006: Trends in artificial intelligence. Springer Berlin Heidelberg, 2006. 854-858. [DOI][Google Scholar]
  • Camels - M. K. Ibrahim, R. S. Ali, "Novel Optimization Algorithm Inspired by Camel Traveling Behavior", Iraq J. Electrical and Electronic Engineering, Vol 12, No 2, 2016.
  • Central force - Richard A. Formato. "Central force optimization: a new metaheuristic with applications in applied electromagnetics." Progress In Electromagnetics Research 77:425-491, 2007. [DOI] [Google Scholar]
  • Charged systems - A. Kaveh, and S. Talatahari. "A novel heuristic optimization method: charged system search." Acta Mechanica 213(3-4):267-289, 2010. [DOI] [Google Scholar]
  • Chemical Reactions - Bilal Alatas. "ACROA: artificial chemical reaction optimization algorithm for global optimization." Expert Systems with Applications 38(10):13170-13180, 2011. [DOI] [Google Scholar]
  • Chickens - Xianbing Meng, Yu Liu, Xiaozhi Gao, and Hengzhen Zhang. "A new bio-inspired algorithm: chicken swarm optimization." In International Conference in Swarm Intelligence, pp. 86-94. Springer International Publishing, 2014. [DOI] [Google Scholar]
  • Clouds - G.W. Yan, Z.J. Hao. "A novel optimization algorithm based on atmosphere clouds model." International Journal of Computational Intelligence and Applications, 12(01), p.1350002, 2013. [DOI] [Google Scholar]
  • Cockroach - I. C. Obagbuwa and A. O. Adewumi, "An Improved Cockroach Swarm Optimization", The Scientific World Journal, 2014. [DOI]
  • Colliding bodies - A. Kaveh and V. R. Mahdavi. "Colliding bodies optimization: a novel meta-heuristic method." Computers & Structures 139:18-27, 2014. [DOI] [Google Scholar]
  • Community of scientists - Alfredo Milani and Valentino Santucci. "Community of scientist optimization: An autonomy oriented approach to distributed optimization." AI Communications 25(2):157-172, 2012. [DOI] [Google Scholar]
  • Consultants - S. Iordache. "Consultant-guided search: a new metaheuristic for combinatorial optimization problems." In Proceedings of the 12th annual conference on Genetic and evolutionary computation (pp. 225-232). ACM, July 2009. [DOI] [Google Scholar]
  • Coral reefs - S. Salcedo-Sanz, J. Del Ser, I. Landa-Torres, S. Gil-López, and J. A. Portilla-Figueras. "The coral reefs optimization algorithm: a novel metaheuristic for efficiently solving optimization problems." The Scientific World Journal 2014 (2014). [DOI] [Google Scholar]
  • Crystal Energy - X. Feng, M. Ma, and H. Yu. "Crystal Energy Optimization Algorithm." Computational Intelligence 32(2):284--322 (2016). [DOI] [Google Scholar]
  • Cuckoos - Xin-She Yang and Suash Deb. "Cuckoo search via Lévy flights." IEEE World Congress on Nature & Biologically Inspired Computing (NaBIC), 2009. [DOI] [Google Scholar]


  • Dogs - C. Subramanian, A.S.S. Sekar and K. Subramanian, "A New Engineering Optimization Method: African Wild Dog Algorithm", International Journal of Soft Computing 8(3), 2013. [Google Scholar]
  • Dolphins
    • Dolphin partners - Y Shiqin et al.. "A Dolphin Partner Optimization." WRI Global Congress on Intelligent Systems, pp 124-128, 2009 [DOI] [Google Scholar]
    • Dolphin echolocation - A. Kaveh, N. Farhoudi. A new optimization method: Dolphin echolocation. Advances in Engineering Software, 59, pp.53-70, 2013. [DOI] [Google Scholar]
  • Dragonflies - S. Mirjalili. "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, 2016. [DOI] [Google Scholar]


  • Eagles - X.S. Yang, S. Deb. "Eagle strategy using Lévy walk and firefly algorithms for stochastic optimization." In Nature Inspired Cooperative Strategies for Optimization (NICSO 2010) (pp. 101-111). Springer Berlin Heidelberg, 2010. [DOI] [Google Scholar]
  • Ecology - R.S. Parpinelli, H.S. Lopes. "An eco-inspired evolutionary algorithm applied to numerical optimization." In Nature and Biologically Inspired Computing (NaBIC), 2011 Third World Congress on (pp. 466-471). IEEE, October 2011. [DOI] [Google Scholar]
  • Electromagnetism - E. Cuevas et al.. "Circle detection using electro-magnetism optimization." Information Sciences, 182(1), pp.40-55, 2012. [DOI] [Google Scholar]
  • Elephants
    • Regular - Suash Deb, Simon Fong, and Zhonghuan Tian. "Elephant Search Algorithm for optimization problems". Proc. 10th IEEE International Conference on Digital Information Management (ICDIM), p. 249-255, 2015. [DOI] [Google Scholar]
    • Flying - Adilson Elias Xavier, Vinicius Layter Xavier. "Flying elephants: a general method for solving non-differentiable problems", J Heuristics (2016) (pp. 649-664), 2016. [DOI]
  • Emotions - Y. Xu, Z. Cui, J. Zeng. "Social emotional optimization algorithm for nonlinear constrained optimization problems." In International Conference on Swarm, Evolutionary, and Memetic Computing (pp. 583-590). Springer Berlin Heidelberg, December 2010. [DOI] [Google Scholar]
  • Experts - V. V. Melo "Kaizen Programming". Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation (GECCO) pp 895-902 (2014) [DOI]


  • FIFA World Cup - N. Razmjooy, M. Khalilpour, M. Ramezani. "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 (2016):1-22, 2016. [DOI] [Google Scholar]
  • Fireflies - Xin-She Yang. "Firefly algorithms for multimodal optimization." Stochastic algorithms: foundations and applications. Springer Berlin Heidelberg, 2009. 169-178. [DOI] [Google Scholar]
  • Fireworks - Y. Tan, Y. Zhu. "Fireworks algorithm for optimization." International Conference in Swarm Intelligence, 355-364. Springer Berlin Heidelberg, 2010. [DOI] [Google Scholar]
  • Fish
    • Catfish - Li-Yeh Chuang, Sheng-Wei Tsai, and Cheng-Hong Yang. "Improved binary particle swarm optimization using catfish effect for feature selection." Expert Systems with Applications 38(10):12699-12707, 2011. [DOI] [Google Scholar]
    • Cuttlefish - A.S. Eesa, A.M. Abdulazeez, Z. Orman. Cuttlefish algorithm - a novel bio-inspired optimization algorithm. International Journal of Scientific and Engineering Research, 4(9), pp.1978-1986, 2013. [IJSER] [Google Scholar]
    • Fish schools - C.J.A. Bastos-Filho et al.. "A novel search algorithm based on fish school behavior." Systems, Man and Cybernetics, 2008. SMC 2008. IEEE International Conference on. IEEE, 2008. [DOI] [Google Scholar]
    • Fish swarms - X.L. Li, J.X. Qian. "Studies on artificial fish swarm optimization algorithm based on decomposition and coordination techniques." J Circuits Syst, 1 (1–6), 2003. [CNKI] [Oriprobe]
  • Flower pollination - Xin-She Yang. "Flower pollination algorithm for global optimization." In International Conference on Unconventional Computing and Natural Computation, pp. 240-249. Springer Berlin Heidelberg, 2012.[DOI] [Google Scholar]
  • Fractals - H. Salimi. Stochastic fractal search: a powerful metaheuristic algorithm. Knowledge-Based Systems, 75, pp.1-18, 2015. [DOI] [Google Scholar]
  • Frogs
    • Frogs leaping - Eusuff and K.E. Lansey. "Optimization of water distribution network design using the shuffled frog leaping algorithm". Journal of Water Resources Planning and Management, 129(3):210–225, 2003. [DOI]
    • Japanese tree frogs - H. Hernández, C. Blum. "Distributed graph coloring: an approach based on the calling behavior of Japanese tree frogs." Swarm Intelligence 6.2 (2012): 117-150. [DOI] [Google Scholar]
  • Fruit Fly - WT Pan "A new fruit fly optimization algorithm: taking the financial distress model as an example." Knowledge-Based Systems, pp 69-74, 2012 [DOI] [Google Scholar]


  • Galaxies - Hamed. Shah-Hosseini "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):132-140, 2011. [DOI] [Google Scholar]
  • Gas molecules - M. Abdechiri, M.R. Meybodi, H. Bahrami. Gases Brownian motion optimization: an algorithm for optimization (GBMO). Applied Soft Computing, 13(5), pp.2932-2946, 2013. [DOI] [Google Scholar]
  • Gene Expression - C. Ferreira. "Gene expression programming in problem solving." In Soft computing and industry (pp. 635-653). Springer London, 2002. [DOI] [Google Scholar]
  • General Relativity - Hamzeh Beiranvand and Esmaeel Rokrok. "General Relativity Search Algorithm: A Global Optimization Approach." International Journal of Computational Intelligence and Applications 14(3):1550017, 2015. [DOI] [Google Scholar]
  • Glow Worms - K. N. Krishnanand and Debasish Ghose. "Glowworm swarm optimization for simultaneous capture of multiple local optima of multimodal functions." Swarm intelligence 3(2):87-124, 2009. [DOI] [Google Scholar]
  • Gravitation - B. Webster and P.J. Bernhard. "A local search optimization algorithm based on natural principles of gravitation.", Proceedings of the international conference on information and knowledge engineering (IKE’03), 2003. 255–261. [IT Florida] [Google Scholar]
  • Great Deluge - G. Dueck "New Optimization Heuristics The Great Deluge Algorithm and the Record-to-Record Travel." Journal of Computational Physics 104(1):86-92, 1993. [DOI] [Google Scholar]
  • Grenades - Ali Ahrari and Ali A. Atai. "Grenade explosion method - a novel tool for optimization of multimodal functions." Applied Soft Computing 10(4):1132-1140, 2010. [DOI] [Google Scholar]
  • Group counselling - M.A. Eita and M. M. Fahmy. "Group counseling optimization: a novel approach." Research and Development in Intelligent Systems XXVI. Springer London, 2010. 195-208. [DOI] [Google Scholar]


  • Hoopoe - Mohammed El-Dosuky, Ahmed El-Bassiouny, Taher Hamza, and Magdy Rashad. "New hoopoe heuristic optimization." International Journal of Science and Advanced Technology 2(9):85-90, 2012. [Google Scholar] [IJSAT]
  • Heart - Abdolreza Hatamlou. "Heart: a novel optimization algorithm for cluster analysis". Progress in Artificial Intelligence, 2(2), pp. 167-173, 2014. [DOI]


  • Invasive weeds - A.R. Mehrabian, C. Lucas. "A novel numerical optimization algorithm inspired from weed colonization." Ecological informatics, 1(4), pp.355-366, 2006. [DOI] [Google Scholar]
  • Interior design and decoration - A.H. Gandomi. Interior search algorithm (ISA): a novel approach for global optimization. ISA transactions, 53(4), pp.1168-1183, 2014. [DOI] [Google Scholar]
  • Ions - B. Javidy, A. Hatamlou, S. Mirjalili. "Ions motion algorithm for solving optimization problems." Applied Soft Computing 32(1):72-79, 2015. [DOI] [Google Scholar]


  • Jaguars - Chin-Chi Chen et al.. "A Novel Metaheuristic: Jaguar Algorithm with Learning Behavior." IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 1595-1600, 2015. [DOI] [Google Scholar]


  • Keshtel - M. Hajiaghaei-Keshteli, M. Aminnayeri, “Solving the integrated scheduling of production rail transportation problem by Keshtel algorithm.” Applied Soft Computing, 25, 184–203, 2014. [DOI]
  • Krill - Amir Hossein Gandomi and Amir Hossein Alavi. "Krill herd: a new bio-inspired optimization algorithm." Communications in Nonlinear Science and Numerical Simulation 17(12):4831-4845, 2012. [DOI] [Google Scholar]


  • Ladybirds - Peng Wang, Zhouquan Zhu, and Shuai Huang. "Seven-spot ladybird optimization: a novel and efficient metaheuristic algorithm for numerical optimization." The Scientific World Journal 2013:378515-378515, 2013. [DOI] [Google Scholar]
  • Lightning - H. Shareef, A.A. Ibrahim, A.H. Mutlag. "Lightning search algorithm". Applied Soft Computing 36(, pp.)1):315-333, 2015. [DOI][Google Scholar]
  • Lion - Maziar Yazdani and Fariborz Jolai. "Lion Optimization Algorithm (LOA): A nature-inspired metaheuristic algorithm." Journal of Computational Design and Engineering 3(1):24-36, 2016. [DOI] [Google Scholar]
  • Locusts - Chen, Stephen. "An analysis of locust swarms on large scale global optimization problems." Artificial Life: Borrowing from Biology. Springer Berlin Heidelberg, 2009. 211-220. [DOI] [Google Scholar]


  • Market - N. Ghorbani, E. Babaei, “Exchange market algorithm.” Applied Soft Computing, 19, 177–187, 2014. [DOI]
  • Metals - S. Kirkpatrick, D. Gelatt Jr., and M. P. Vecchi. "Optimization by simulated annealing". Science, 220(4598):671–680, 1983. [Google Scholar]
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  • Monkeys
    • Monkeys foraging - Antonio Mucherino and Onur Seref. "Monkey search: a novel metaheuristic search for global optimization." Data Mining, Systems Analysis and Optimization in Biomedicine 953(1). AIP Publishing, 2007. [DOI] [Google Scholar]
    • Spider Monkeys - Jagdish Chand Bansal et al.. "Spider monkey optimization algorithm for numerical optimization." Memetic Computing 6(1):31-47, 2014. [DOI] [Google Scholar]
  • Mountain climbers - L.M. Zhang, C. Dahlmann, Y. Zhang. "Human-inspired algorithms for continuous function optimization." In Intelligent Computing and Intelligent Systems, 2009. ICIS 2009. IEEE International Conference on (Vol. 1, pp. 318-321). IEEE, November 2009. [DOI] [Google Scholar]
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  • Multiverse - S. Mirjalili, S. M. Mirjalili, A. Hatamlou, “Multi-Verse Optimizer: a nature-inspired algorithm for global optimization.” Neural Computing & Applications, 1-19, 2015. [DOI]
  • Musicians - Zong Woo Geem, Joong Hoon Kim, and G. V. Loganathan. "A new heuristic optimization algorithm: harmony search." Simulation 76(2):60-68, 2001. [DOI][Google Scholar]



  • Optics - A.H. Kashan. A new metaheuristic for optimization: optics inspired optimization (OIO). Computers & Operations Research, 55, pp.99-125, 2015. [DOI] [Google Scholar]


  • Paddy fields - U. Premaratne, J. Samarabandu, T. Sidhu. "A new biologically inspired optimization algorithm." In 2009 international conference on industrial and information systems (ICIIS) (pp. 279-284). IEEE, December 2009 [DOI] [Google Scholar]
  • Parliamentary head elections - A. Borji, A new global optimization algorithm inspired by parliamentary political competitions. In Mexican International Conference on Artificial Intelligence (pp. 61-71). Springer Berlin Heidelberg, November 2007. [DOI] [Google Scholar]
  • Penguins - Y. Gheraibia, A. Moussaoui. Penguins search optimization algorithm (PeSOA). In International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (pp. 222-231). Springer Berlin Heidelberg, June 2013. [DOI] [Google Scholar]
  • Plants
    • Growing- Jun Li, Zhihua Cui and Zhongzhi Shi. "An improved artificial plant optimization algorithm for coverage problem in WSN." Sensor Letters 10(8):1874-1878, 2012. [DOI] [Google Scholar]
    • Propagation - Muhammad Sulaiman et al.. "A plant propagation algorithm for constrained engineering optimisation problems." Mathematical Problems in Engineering 2014:627416, 2014. [DOI] [Google Scholar]
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  • Political Imperialism - Esmaeil Atashpaz-Gargari and Caro Lucas. "Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition." Evolutionary Computation, 2007. CEC 2007. IEEE Congress on. IEEE, 2007. [DOI] [Google Scholar]
  • Politicians - J.M.L. Melvix. "Greedy Politics Optimization: Metaheuristic inspired by political strategies adopted during state assembly elections". In Advance Computing Conference (IACC), 2014 IEEE International (pp. 1157-1162). IEEE, February 2014. [DOI] [Google Scholar]



  • Rays of light - A. Kaveh and M. Khayatazad. "A new meta-heuristic method: ray optimization." Computers & Structures 112:283-294, 2012. [DOI] [Google Scholar]
  • Reincarnation - A. Sharma. A new optimizing algorithm using reincarnation concept. In Computational Intelligence and Informatics (CINTI), 2010 11th International Symposium on (pp. 281-288). IEEE, November 2010. [DOI] [Google Scholar]
  • River formation - P. Rabanal, I. Rodríguez, F. Rubio. "Using river formation dynamics to design heuristic algorithms." In International Conference on Unconventional Computation (pp. 163-177). Springer Berlin Heidelberg, August 2007. [DOI] [Google Scholar]
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  • Roots - F. Merrikh-Bayat. "The runner-root algorithm: A metaheuristic for solving unimodal and multimodal optimization problems inspired by runners and roots of plants in nature." Applied Soft Computing 33:292-303, 2015. [DOI] [Google Scholar]


  • Salmon migrations - A. Mozaffari, A. Fathi, S. Behzadipour. "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, 2012. [Google Scholar]
  • Scientific method - D. Felipe, E. Goldbarg, and M. Goldbarg. "Scientific algorithms for the Car Renter Salesman Problem." IEEE Congress on Evolutionary Computation (CEC), pp. 873-879. IEEE, 2014. [DOI] [Google Scholar]
  • Sharks - Oveis Abedinia, Nima Amjady, and Ali Ghasemi. "A new metaheuristic algorithm based on shark smell optimization." Complexity, 2014. [DOI] [Google Scholar]
  • Sheep flocks - Hyunchul Kim and Byungchul Ahn. "A new evolutionary algorithm based on sheep flocks heredity model." In Communications, Computers and signal Processing, 2001. PACRIM. 2001 IEEE Pacific Rim Conference on, vol. 2, pp. 514-517. IEEE, 2001. [DOI] [Google Scholar]
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  • Spirals - K. Tamura, K. Yasuda. "Spiral Dynamics Inspired Optimization." Journal of Advanced Computational Intelligence and Intelligent Informatics, 15(8), pp.1116-1122, 2011. [DOI] [Google Scholar]
  • Soccer - H.D. Purnomo, H.-M. Wee. "Soccer game optimization: an innovative integration of evolutionary algorithm and swarm intelligence algorithm." Meta-Heuristics optimization algorithms in engineering, business, economics, and finance. IGI Global, Pennsylvania (2012). [DOI] [Google Scholar]
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  • Sperm - Raouf, Hezam, "Sperm motility algorithm: a novel metaheuristic approach for global optimisation." International Journal of Operational Research (IJOR), 28-2, 2017. [DOI]
  • Sports championships - Ali Husseinzadeh Kashan. "League Championship Algorithm (LCA): An algorithm for global optimization inspired by sport championships." Applied Soft Computing 16:171-200, 2014. [DOI] [Google Scholar]
  • Swallows - Mehdi Neshat, Ghodrat Sepidnam, and Mehdi Sargolzaei. "Swallow swarm optimization algorithm: a new method to optimization." Neural Computing and Applications 23(2):429-454, 2013. [DOI] [Google Scholar]
  • Symbiotic organisms - M.Y. Cheng, D. Prayogo. Symbiotic organisms search: a new metaheuristic optimization algorithm. Computers & Structures, 139, pp.98-112, 2014. [DOI] [Google Scholar]


  • Teachers - R. V. Rao, V. J. Savsani, D. P. Vakharia, “Teaching-learning-based optimization: a novel method for constrained mechanical design optimization problems.” Computer-Aided Design, 43, (3), 303–315, 2011. [DOI]
  • Termites - R. Hedayatzadeh et al.. "Termite colony optimization: A novel approach for optimizing continuous problems." In 2010 18th Iranian Conference on Electrical Engineering (pp. 553-558). IEEE, May 2010. [DOI] [Google Scholar]
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  • Tumors - Deyu Tang et al.. "ITGO: Invasive tumor growth optimization algorithm." Applied Soft Computing 36:670-698, 2015. [DOI] [Google Scholar]



  • Virus - Cortés, P., García, J.M., Muñuzuri, J. and Onieva, L. "Viral systems: A new bio-inspired optimisation approach." Computers & Operations Research. 35(9):2840-2860, 2008. [DOI] [Google Scholar]
  • Vortexes - B. Dogan, T. Olmez, "A new metaheuristic for numerical function optimization: Vortex Search Algorithm". Information Sciences 293 pp. 125-145 (2015) [DOI]
  • Vultures - Chiranjib Sur, Sanjeev Sharma, and Anupam Shukla. "Egyptian vulture optimization algorithm - a new nature inspired meta-heuristics for knapsack problem." The 9th International Conference on Computing and Information Technology (IC2IT). pp. 227-237, Springer Berlin Heidelberg, 2013. [DOI] [Google Scholar]


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  • Water
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("the Zoo Keepers")


(at least one contribution to the bestiary, in alphabetical order)

  • Adré Steyn - University of Stellenbosch, South Africa
  • André Maravilha - UFMG, Brazil
  • Ciniro Nametala - UFMG, Brazil
  • Fabio Daolio - University of Stirling, Scotland UK
  • Fernanda Takahashi - UFMG, Brazil
  • Fernando Otero - University of Kent, England UK
  • Fillipe Goulart - UFMG, Brazil
  • Iztok Fister Jr. - University of Maribor, Slovenia
  • Kenneth Sörensen - University of Antwerp, Belgium
  • Lars Magnus Hvattum - Molde University College, Norway
  • Marc Sevaux - Université de Bretagne-Sud, France
  • 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
  • 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

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 ploriferation of metaphors in EC research. A fantastic parody of this whole metaphor craze can be read here. Highly recommended!


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):

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