Document Type : Review Paper

Authors

1 Department of Industrial Management, Science and Research Branch, Islamic Azad University, Tehran, Iran.

2 Department of Industrial Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran.

10.22105/jarie.2021.238926.1180

Abstract

Conventional and classical optimization methods are not efficient enough to deal with complicated, NP-hard, high-dimensional, non-linear, and hybrid problems. In recent years, the application of meta-heuristic algorithms for such problems increased dramatically and it is widely used in various fields. These algorithms, in contrast to exact optimization methods, find the solutions which are very close to the global optimum solution as possible, in such a way that this solution satisfies the threshold constraint with an acceptable level. Most of the meta-heuristic algorithms are inspired by natural phenomena. In this research, a comprehensive review on meta-heuristic algorithms is presented to introduce a large number of them (i.e. about 110 algorithms). Moreover, this research provides a brief explanation along with the source of their inspiration for each algorithm. Also, these algorithms are categorized based on the type of algorithms (e.g. swarm-based, evolutionary, physics-based, and human-based), nature-inspired vs non-nature-inspired based, population-based vs single-solution based. Finally, we present a novel classification of meta-heuristic algorithms based on the country of origin.

Keywords

Main Subjects

[1]         Safavi, S. A. A., Pour Jafarian, N., & Safavi, S. A. (2014). Optimization based on meta-heuristic algorithms. Pajuheshgarane Nashre Daneshgahi. (In Persian). https://www.adinehbook.com/gp/product/9648771782
[2]         Alam Tabriz, A., Zandieh, M., & Mohammad Rahimi, A. (2013). Meta-heuristic algorithms in hybrid optimization. Saffar-Eshraiggi. (In Persian). https://www.adinehbook.com/gp/product/9643882174
[3]         Weise, T. (2009). Global optimization algorithms-theory and application. Self-published thomas weise.
[4]         Beheshti, Z., & Shamsuddin, S. M. H. (2013). A review of population-based meta-heuristic algorithms. Int. j. adv. soft comput. appl5(1), 1-35.
[5]         Mohammad Pour Zarandi, M. E. (2013). Nonlinear optimization. Tehran University. (In Persian). https://www.adinehbook.com/gp/product/9640364754
[6]         Toroslu, I. H., & Cosar, A. (2004). Dynamic programming solution for multiple query optimization problem. Information processing letters92(3), 149-155.
[7]         Balev, S., Yanev, N., Fréville, A., & Andonov, R. (2008). A dynamic programming based reduction procedure for the multidimensional 0–1 knapsack problem. European journal of operational research186(1), 63-76.
[8]         Martí, R., Gallego, M., & Duarte, A. (2010). A branch and bound algorithm for the maximum diversity problem. European journal of operational research200(1), 36-44.
[9]         Tavakoli Moghaddam, R., Norouzi, N., Kalami, S. M., & Salambakhsh, A. (2013). Meta-heuristic algorithms, from theoretical and implementation perspective in Matlab. Islamic Azad University. (In Persian). https://www.adinehbook.com/gp/product/9641024934
[10]      Talbi, E. G. (2009). Metaheuristics: from design to implementation (Vol. 74). John Wiley & Sons.
[11]      Laporte, G., & Osman, I. H. (1995). Routing problems: A bibliography. Annals of operations research61(1), 227-262.
[12]      Voss, S., Martello, S., Osman, I. H., & Roucairol, C. (1999). Meta-Heuristics - Advances and Trends in Local Search Paradigms for Optimization. Dordrecht: Kluwer Academic Publishers. DOI: 10.1007/978-1-4615-5775-3
[13]      Badrloo, S., & Husseinzadeh Kashan, A. (2019). Combinatorial optimization of permutation-based quadratic assignment problem using optics inspired optimization. Journal of applied research on industrial engineering6(4), 314-332.
[14]      Kanagasabai, L. (2020). Factual power loss reduction by augmented monkey optimization algorithm. International journal of research in industrial engineering9(1), 1-12.
[15]      Shahabi, F., Pourahangarian, F., & Beheshti, H. (2019). A multilevel image thresholding approach based on crow search algorithm and Otsu method. Decisions and operations research4(1), 33-41. (In Persian). http://dx.doi.org/10.22105/dmor.2019.88580
[16]      Ghahramani Nahr, J. (2019). Improve the efficiency and effectiveness of the closed loop supply chain: Wall optimization algorithm and new coding based on priority approach. Decisions and operations research, 4(4), 299-315. (In Persian). http://dx.doi.org/10.22105/dmor.2020.206930.1132
[17]      Sharifzadeh, H., & Amjady, N. (2014). a Review of metaheuristic algorithms in optimization. Journal of modeling in engineering12(38), 27-43. (In Persian). DOI: 10.22075/jme.2017.1677
[18]      Fogel, D. B., & Fogel, L. J. (1995, September). An introduction to evolutionary programming. European conference on artificial evolution (pp. 21-33). Springer, Berlin, Heidelberg.
[19]    Holland, J. (1975). Adaptation in natural and artificial systems: an introductory analysis with application to biology. MIT Press
[20]      Glover, F. (1977). Heuristics for integer programming using surrogate constraints. Decision sciences8(1), 156-166.
[21]      Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. science220(4598), 671-680.
[22]      Glover, F. (1986). Future paths for integer programming and links to artificial intelligence. Computers and operations research13(5), 533-549.
[23]      Reynolds, R. G. (1994, February). An introduction to cultural algorithms. Proceedings of the third annual conference on evolutionary programming, (24), (pp. 131-139). River Edge, NJ: World Scientific.
[24]      Kennedy, J., & Eberhart, R. (1995, November). Particle swarm optimization. Proceedings of ICNN'95-international conference on neural networks, (4), (pp. 1942-1948). IEEE.
[25]      Dorigo, M., Maniezzo, V., & Colorni, A. (1996). Ant system: optimization by a colony of cooperating agents. IEEE transactions on systems, man, and cybernetics, part b (Cybernetics)26(1), 29-41.
[26]      Storn, R., & Price, K. (1997). Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of global optimization11(4), 341-359.
[27]      Mladenović, N., & Hansen, P. (1997). Variable neighborhood search. Computers and operations research24(11), 1097-1100.
[28]      Kim, H., & Ahn, B. (2001, August). A new evolutionary algorithm based on sheep flocks heredity model. 2001 IEEE pacific rim conference on communications, computers and signal processing (IEEE Cat. No. 01CH37233), (2), (pp. 514-517). IEEE.
[29]      Geem, Z. W., Kim, J. H., & Loganathan, G. V. (2001). A new heuristic optimization algorithm: harmony search. simulation76(2), 60-68.
[30]      Passino, K. M. (2002). Biomimicry of bacterial foraging for distributed optimization and control. IEEE control systems magazine22(3), 52-67.
[31]      Xie, X. F., Zhang, W. J., & Yang, Z. L. (2002, November). Social cognitive optimization for nonlinear programming problems. Proceedings. international conference on machine learning and cybernetics, (2), (pp. 779-783). IEEE.
[32]      Eusuff, M. M., & Lansey, K. E. (2003). Optimization of water distribution network design using the shuffled frog leaping algorithm. Journal of water resources planning and management129(3), 210-225.
[33]      Birbil, Ş. İ., & Fang, S. C. (2003). An electromagnetism-like mechanism for global optimization. Journal of global optimization25(3), 263-282.
[34]      Hsiao, Y. T., Chuang, C. L., Jiang, J. A., & Chien, C. C. (2005, October). A novel optimization algorithm: space gravitational optimization. 2005 IEEE international conference on systems, man and cybernetics, (3), (pp. 2323-2328). IEEE.
[35]      Sacco, W. F., & de Oliveira, C. R. (2005). A new stochastic optimization algorithm based on particle collisions. Transactions92(1), 657-659.
[36]      Erol, O. K., & Eksin, I. (2006). A new optimization method: big bang–big crunch. Advances in engineering software37(2), 106-111.
[37]      He, S., Wu, Q. H., & Saunders, J. R. (2006, July). A novel group search optimizer inspired by animal behavioural ecology. 2006 IEEE international conference on evolutionary computation (pp. 1272-1278). IEEE.
[38]      Mehrabian, A. R., & Lucas, C. (2006). A novel numerical optimization algorithm inspired from weed colonization. Ecological informatics1(4), 355-366.
[39]      Du, H., Wu, X., & Zhuang, J. (2006, September). Small-world optimization algorithm for function optimization. International conference on natural computation (pp. 264-273). Berlin, Heidelberg: Springer.
[40]      Chu, S. C., Tsai, P. W., & Pan, J. S. (2006, August). Cat swarm optimization. Pacific rim international conference on artificial intelligence (pp. 854-858). Berlin, Heidelberg: Springer.
[41]      Karci, A., & Alatas, B. (2006, September). Thinking capability of saplings growing up algorithm. International conference on intelligent data engineering and automated learning (pp. 386-393). Berlin, Heidelberg: Springer.
[42]      Atashpaz-Gargari, E., & Lucas, C. (2007, September). Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. 2007 IEEE congress on evolutionary computation (pp. 4661-4667). IEEE.
[43]      Karaboga, D., & Basturk, B. (2007). A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of global optimization39(3), 459-471.
[44]      Formato, R. A. (2007). Central force optimization: a new metaheuristic with applications in applied electromagnetics. Prog Electromagn Res, 77(1), 425–491.
[45]      Chuang, C. L., & Jiang, J. A. (2007, September). Integrated radiation optimization: inspired by the gravitational radiation in the curvature of space-time. 2007 IEEE congress on evolutionary computation (pp. 3157-3164). IEEE.
[46]      Lamberti, L., & Pappalettere, C. (2007). Weight optimization of skeletal structures with multi-point simulated annealing. Computer modeling in engineering and sciences18(3), 183-221.
[47]      Rabanal, P., Rodríguez, I., & Rubio, F. (2007, August). Using river formation dynamics to design heuristic algorithms. In Akl S. G., Calude C. S., Dinneen M. J., Rozenberg G., Wareham H. T. (Eds) Unconventional computation. UC 2007. Lecture Notes in Computer Science, vol 4618. (pp. 163-177). Berlin, Heidelberg: Springer.
[48]      Kripka, M., & Kripka, R. M. L. (2008, June). Big crunch optimization method. International conference on engineering optimization, Brazil (pp. 1-5).
[49]      Simon, D. (2008). Biogeography-based optimization. IEEE transactions on evolutionary computation12(6), 702-713.
[50]      Yang, X. S. (2009, October). Firefly algorithms for multimodal optimization. International symposium on stochastic algorithms (pp. 169-178). Springer, Berlin, Heidelberg.
[51]      Premaratne, U., Samarabandu, J., & Sidhu, T. (2009, December). A new biologically inspired optimization algorithm. 2009 international conference on industrial and information systems (ICIIS) (pp. 279-284). IEEE.
[52]      Rashedi, E., Nezamabadi-Pour, H., & Saryazdi, S. (2009). GSA: a gravitational search algorithm. Information sciences179(13), 2232-2248.
[53]      Yang, X. S., & Deb, S. (2009, December). Cuckoo search via Lévy flights. 2009 World congress on nature & biologically inspired computing (NaBIC) (pp. 210-214). IEEE.
[54]      Oftadeh, R., & Mahjoob, M. J. (2009, September). A new meta-heuristic optimization algorithm: Hunting Search. 2009 fifth international conference on soft computing, computing with words and perceptions in system analysis, decision and control (pp. 1-5). IEEE.
[55]      Shah-Hosseini, H. (2009). The intelligent water drops algorithm: a nature-inspired swarm-based optimization algorithm. International journal of bio-inspired computation1(1-2), 71-79.
[56]      Xie, L., Zeng, J., & Cui, Z. (2009, December). General framework of artificial physics optimization algorithm. 2009 world congress on nature & biologically inspired computing (NaBIC) (pp. 1321-1326). IEEE.
[57]      Das, S., Chowdhury, A., & Abraham, A. (2009, May). A bacterial evolutionary algorithm for automatic data clustering. 2009 IEEE congress on evolutionary computation (pp. 2403-2410). IEEE.
[58]      Zhang, L. M., Dahlmann, C., & Zhang, Y. (2009, November). Human-inspired algorithms for continuous function optimization. 2009 IEEE international conference on intelligent computing and intelligent systems, (1), (pp. 318-321). IEEE.
[59]      Kashan, A. H. (2009, December). League championship algorithm: a new algorithm for numerical function optimization. 2009 international conference of soft computing and pattern recognition (pp. 43-48). IEEE.     
[60]       Chen, S. (2009, May). Locust Swarms-A new multi-optima search technique. 2009 IEEE congress on evolutionary computation (pp. 1745-1752). IEEE.
[61]      Iordache, S. (2010, July). Consultant-guided search: a new metaheuristic for combinatorial optimization problems. Proceedings of the 12th annual conference on genetic and evolutionary computation (pp. 225-232).
[62]      Yang, X. S. (2010). A new metaheuristic bat-inspired algorithm. In González J. R., Pelta D. A., Cruz C., Terrazas G., Krasnogor N. (Eds) Nature inspired cooperative strategies for optimization (NICSO 2010). Studies in computational intelligence, vol 284 (pp. 65-74). Berlin, Heidelberg: Springer.
[63]      Kaveh, A., & Talatahari, S. (2010). A novel heuristic optimization method: charged system search. Acta mechanica213(3), 267-289. https://doi.org/10.1007/s00707-009-0270-4
[64]      Lam, A. Y., & Li, V. O. (2009). Chemical-reaction-inspired metaheuristic for optimization. IEEE transactions on evolutionary computation14(3), 381-399.
[65]      Yang, X. S., & Deb, S. (2010). Eagle strategy using Lévy walk and firefly algorithms for stochastic optimization. In González J. R., Pelta D. A., Cruz C., Terrazas G., Krasnogor N. (Eds) Nature inspired cooperative strategies for optimization (NICSO 2010). Studies in computational intelligence, vol 284. (pp. 101-111). Berlin, Heidelberg: Springer.
[66]      Eita, M. A., & Fahmy, M. M. (2010). Group counseling optimization: a novel approach. In Bramer M., Ellis R., Petridis M. (Eds) Research and development in intelligent systems XXVI (pp. 195-208). London Springer.
[67]      Xu, Y., Cui, Z., & Zeng, J. (2010, December). Social emotional optimization algorithm for nonlinear constrained optimization problems. International conference on swarm, evolutionary, and memetic computing (pp. 583-590). Berlin, Heidelberg: Springer.
[68]      Shah-Hosseini, H. (2011, October). Otsu's criterion-based multilevel thresholding by a nature-inspired metaheuristic called galaxy-based search algorithm. 2011 third world congress on nature and biologically inspired computing (pp. 383-388). IEEE.
[69]      Shah-Hosseini, H. (2011). Principal components analysis by the galaxy-based search algorithm: a novel metaheuristic for continuous optimisation. International journal of computational science and engineering6(1-2), 132-140.
[70]      Tamura, K., & Yasuda, K. (2011). Spiral dynamics inspired optimization. Journal of advanced computational intelligence and intelligent informatics15(8), 1116-1122.
[71]      Rao, R. V., Savsani, V. J., & Vakharia, D. P. (2011). Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design43(3), 303-315.
[72]      Shayeghi, H., & Dadashpour, J. (2012). Anarchic society optimization based PID control of an automatic voltage regulator (AVR) system. Electrical and electronic engineering2(4), 199-207.
[73]      Sakulin, A., & Puangdownreong, D. (2012). A novel meta-heuristic optimization algorithm: current search. Proceedings of the 11th WSEAS international conference on artificial intelligence, knowledge engineering and data bases (pp. 374-389). World Scientific and Engineering Academy and Society (WSEAS)Stevens PointWisconsinUnited States. https://dl.acm.org/doi/proceedings/10.5555/2183067
[74]      Eskandar, H., Sadollah, A., Bahreininejad, A., & Hamdi, M. (2012). Water cycle algorithm–A novel metaheuristic optimization method for solving constrained engineering optimization problems. Computers and structures110-111, 151-166. https://doi.org/10.1016/j.compstruc.2012.07.010
[75]      Tang, R., Fong, S., Yang, X. S., & Deb, S. (2012, August). Wolf search algorithm with ephemeral memory. Seventh international conference on digital information management (ICDIM 2012) (pp. 165-172). IEEE.
[76]      Sadollah, A., Bahreininejad, A., Eskandar, H., & Hamdi, M. (2012). Mine blast algorithm for optimization of truss structures with discrete variables. Computers and structures102-103, 49-63. https://doi.org/10.1016/j.compstruc.2012.03.013
[77]      Yan, G. W., & Hao, Z. J. (2013). A novel optimization algorithm based on atmosphere clouds model. International journal of computational intelligence and applications12(01), 1350002.
[78]      Hatamlou, A. (2013). Black hole: A new heuristic optimization approach for data clustering. Information sciences222(3), 175-184. https://doi.org/10.1016/j.ins.2012.08.023
[79]      Sur, C., Sharma, S., & Shukla, A. (2013). Egyptian vulture optimization algorithm–a new nature inspired meta-heuristics for knapsack problem. The 9th international conference on computing and informationtechnology (IC2IT2013) (pp. 227-237). Berlin, Heidelberg: Springer, 
[80]      Gheraibia, Y., & Moussaoui, A. (2013, June). Penguins search optimization algorithm (PeSOA). International conference on industrial, engineering and other applications of applied intelligent systems (pp. 222-231). Berlin, Heidelberg: Springer.
[81]      Neshat, M., Sepidnam, G., & Sargolzaei, M. (2013). Swallow swarm optimization algorithm: a new method to optimization. Neural computing and applications23(2), 429-454.
[82]      Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in engineering software69, 46-61. https://doi.org/10.1016/j.advengsoft.2013.12.007
[83]      Osaba, E., Diaz, F., & Onieva, E. (2014). Golden ball: a novel meta-heuristic to solve combinatorial optimization problems based on soccer concepts. Applied intelligence41(1), 145-166.
[84]      Li, X., Zhang, J., & Yin, M. (2014). Animal migration optimization: an optimization algorithm inspired by animal migration behavior. Neural computing and applications24(7), 1867-1877.
[85]      Moosavian, N., & Roodsari, B. K. (2014). Soccer league competition algorithm: A novel meta-heuristic algorithm for optimal design of water distribution networks. Swarm and evolutionary computation17, 14-24. https://doi.org/10.1016/j.swevo.2014.02.002
[86]      Moosavian, N., & Roodsari, B. K. (2013). Soccer league competition algorithm, a new method for solving systems of nonlinear equations. International journal of intelligence science4(01), 7-16. http://dx.doi.org/10.4236/ijis.2014.41002
[87]      Meng, X., Liu, Y., Gao, X., & Zhang, H. (2014, October). A new bio-inspired algorithm: chicken swarm optimization. International conference in swarm intelligence (pp. 86-94). Cham: Springer.
[88]      Ghaemi, M., & Feizi-Derakhshi, M. R. (2014). Forest optimization algorithm. Expert systems with applications41(15), 6676-6687.
[89]      Hatamlou, A. (2014). Heart: a novel optimization algorithm for cluster analysis. Progress in artificial intelligence2(2-3), 167-173.
[90]      De Melo, V. V. (2014, July). Kaizen programming. Proceedings of the 2014 annual conference on genetic and evolutionary computation (pp. 895-902). https://doi.org/10.1145/2576768.2598264
[91]      Ghorbani, N., & Babaei, E. (2014). Exchange market algorithm. Applied soft computing19, 177-187. https://doi.org/10.1016/j.asoc.2014.02.006
[92]      Odili, J. B., Kahar, M. N. M., & Anwar, S. (2015). African buffalo optimization: a swarm-intelligence technique. Procedia computer science76, 443-448. https://doi.org/10.1016/j.procs.2015.12.291
[93]      Wang, G. G., Deb, S., & Coelho, L. D. S. (2015, December). Elephant herding optimization. 2015 3rd international symposium on computational and business intelligence (ISCBI) (pp. 1-5). IEEE.
[94]      Javidy, B., Hatamlou, A., & Mirjalili, S. (2015). Ions motion algorithm for solving optimization problems. Applied soft computing32(1), 72-79.
[95]      Beiranvand, H., & Rokrok, E. (2015). General relativity search algorithm: a global optimization approach. International journal of computational intelligence and applications14(03), 1550017.
[96]      Chen, C. C., Tsai, Y. C., Liu, I. I., Lai, C. C., Yeh, Y. T., Kuo, S. Y., & Chou, Y. H. (2015, October). A novel metaheuristic: Jaguar algorithm with learning behavior. 2015 IEEE international conference on systems, man, and cybernetics (pp. 1595-1600). IEEE.
[97]      Kashan, A. H. (2015). A new metaheuristic for optimization: optics inspired optimization (OIO). Computers and operations research55, 99-125. https://doi.org/10.1016/j.cor.2014.10.011
[98]      Merrikh-Bayat, F. (2015). The runner-root algorithm: a metaheuristic for solving unimodal and multimodal optimization problems inspired by runners and roots of plants in nature. Applied soft computing33, 292-303. https://doi.org/10.1016/j.asoc.2015.04.048
[99]      Doğan, B., & Ölmez, T. (2015). A new metaheuristic for numerical function optimization: Vortex Search algorithm. Information sciences293, 125-145. https://doi.org/10.1016/j.ins.2014.08.053
[100]  Salimi, H. (2015). Stochastic fractal search: a powerful metaheuristic algorithm. Knowledge-based systems75, 1-18. https://doi.org/10.1016/j.knosys.2014.07.025
[101]  Tilahun, S. L., & Ong, H. C. (2015). Prey-predator algorithm: a new metaheuristic algorithm for optimization problems. International journal of information technology and decision making14(06), 1331-1352.
[102]  Zheng, Y. J. (2015). Water wave optimization: a new nature-inspired metaheuristic. Computers and operations research55(1), 1-11.
[103]  Findik, O. (2015). Bull optimization algorithm based on genetic operators for continuous optimization problems. Turkish journal of electrical engineering and computer sciences, 23(1), 2225-2239.
[104]  Deb, S., Fong, S., & Tian, Z. (2015, October). Elephant search algorithm for optimization problems. 2015 tenth international conference on digital information management (ICDIM) (pp. 249-255). IEEE.
[105]  Mirjalili, S. (2015). The ant lion optimizer. Advances in engineering software83, 80-98. https://doi.org/10.1016/j.advengsoft.2015.01.010
[106]  Yazdani, M., & Jolai, F. (2016). Lion optimization algorithm (LOA): a nature-inspired metaheuristic algorithm. Journal of computational design and engineering3(1), 24-36.
[107]  Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in engineering software95, 51-67. https://doi.org/10.1016/j.advengsoft.2016.01.008
[108]  Topal, A. O., & Altun, O. (2016). A novel meta-heuristic algorithm: dynamic virtual bats algorithm. Information sciences354, 222-235. https://doi.org/10.1016/j.ins.2016.03.025
[109]  Kaveh, A., & Zolghadr, A. (2016). A novel meta-heuristic algorithm: tug of war optimization. International journal of optimization in civil engineering6(4), 469-492.
[110]  Liang, Y. C., & Cuevas Juarez, J. R. (2016). A novel metaheuristic for continuous optimization problems: Virus optimization algorithm. Engineering optimization48(1), 73-93.
[111]  Li, M. D., Zhao, H., Weng, X. W., & Han, T. (2016). A novel nature-inspired algorithm for optimization: Virus colony search. Advances in engineering software92, 65-88. https://doi.org/10.1016/j.advengsoft.2015.11.004
[112]  Askarzadeh, A. (2016). A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Computers and structures169, 1-12.
[113]  Mirjalili, S. (2016). Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural computing and applications27(4), 1053-1073.
[114]  Ibrahim, M. K., & Ali, R. S. (2016). Novel optimization algorithm inspired by camel traveling behavior. Iraq journal electrical and electronic engineering12(2), 167-177.
[115]  Kaveh, A., & Bakhshpoori, T. (2016). Water evaporation optimization: a novel physically inspired optimization algorithm. Computers and structures167, 69-85. https://doi.org/10.1016/j.compstruc.2016.01.008
[116]  Kaveh, A., & Dadras, A. (2017). A novel meta-heuristic optimization algorithm: thermal exchange optimization. Advances in engineering software110, 69-84. https://doi.org/10.1016/j.advengsoft.2017.03.014
[117]  Tabari, A., & Ahmad, A. (2017). A new optimization method: electro-search algorithm. Computers and chemical engineering103, 1-11. https://doi.org/10.1016/j.compchemeng.2017.01.046
[118]  Saremi, S., Mirjalili, S., & Lewis, A. (2017). Grasshopper optimisation algorithm: theory and application. Advances in engineering software105(1), 30-47.
[119]  Raouf, O. A., & Hezam, I. M. (2017). Sperm motility algorithm: a novel metaheuristic approach for global optimisation. International journal of operational research28(2), 143-163.
[120]  Wang, T., & Yang, L. (2018). Beetle swarm optimization algorithm: Theory and application.  arXiv:1808.00206v2
[121]  Ismail, F. H., Houssein, E. H., & Hassanien, A. E. (2018, September). Chaotic bird swarm optimization algorithm. International conference on advanced intelligent systems and informatics (pp. 294-303). Cham: Springer.
[122]  Arora, S., & Singh, S. (2019). Butterfly optimization algorithm: a novel approach for global optimization. Soft computing23(3), 715-734.
[123]  Arora, S., & Anand, P. (2019). Chaotic grasshopper optimization algorithm for global optimization. Neural computing and applications31(8), 4385-4405.
[124]  Qiao, W., & Yang, Z. (2019). Solving large-scale function optimization problem by using a new metaheuristic algorithm based on quantum dolphin swarm algorithm. IEEE access7(1), 138972-138989. DOI: 10.1109/ACCESS.2019.2942169.
[125]  Harifi, S., Khalilian, M., Mohammadzadeh, J., & Ebrahimnejad, S. (2019). Emperor penguins colony: a new metaheuristic algorithm for optimization. Evolutionary intelligence12(2), 211-226.
[126]  Dehghani, M., Montazeri, Z., Malik, O. P., Givi, H., & Guerrero, J. M. (2020). Shell game optimization: a novel game-based algorithm. International journal of intelligent engineering and systems13(3), 246-255.
[127]  Dehghani, M., Montazeri, Z., Givi, H., Guerrero, J. M., & Dhiman, G. (2020). Darts game optimizer: a new optimization technique based on darts game. Int. J. Intell. Eng. Syst13(1), 286-294.
[128]  Braik, M., Sheta, A., & Al-Hiary, H. (2020). A novel meta-heuristic search algorithm for solving optimization problems: capuchin search algorithm. Neural computing and applications, 1-33. https://doi.org/10.1007/s00521-020-05145-6
[129]  Fathollahi-Fard, A. M., Hajiaghaei-Keshteli, M., & Tavakkoli-Moghaddam, R. (2020). Red deer algorithm (RDA): a new nature-inspired meta-heuristic. Soft computing, 19(1), 1-29.
[130]  Birattari, M., Paquete, L., Stützle, T., & Varrentrapp, K. (2001). Classification of metaheuristics and design of experiments for the analysis of components (AIDA-01-05). Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.12.4407&rep=rep1&type=pdf
[131]  Fister Jr, I., Yang, X. S., Fister, I., Brest, J., & Fister, D. (2013). A brief review of nature-inspired algorithms for optimization. arXiv:1307.4186v1