[1] Wolpert DH, Macready WG. (1997). No free lunch theorems for optimization. IEEE transactions on evolutionary computation;1(1):67-82.
[2] Darwin C. (2004). On the Origin of Species, 1859. Rivista di biologia.
[3] MiarNaeimi F, Azizyan G, Rashki M. (2018). Multi-level cross entropy optimizer (MCEO): an evolutionary optimization algorithm for engineering problems. Engineering with Computers. 34(4):719-739.
[4] Chou J-S, Truong D-N. (2021). A novel metaheuristic optimizer inspired by behavior of jellyfish in ocean. Applied Mathematics and Computation. 389:125535.
[5] Evans SM. (1986). Control of marine pollution generated by offshore oil and gas exploration and exploitation: the Scotian Shelf. Marine Policy.10(4):258-270.
[6] Kaveh A. (2016). Advances in Metaheuristic Algorithms for Optimal Design of Structures, Second Edition. Springer.
[7] Yang X-S. (2010). Engineering Optimization: An Introduction with Metaheuristic Applications. John Wiley & Sons.
[8] Mirjalili S, Mirjalili SM, Lewis A. (2014). Grey wolf optimizer. Advances in engineering software. 69:46-61.
[9] Holland JH. (1992). Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. MIT press, 0262581116.
[10] Kirkpatrick S, Gelatt CD, Vecchi MP. (1983). Optimization by simulated annealing. Science (80). 220(4598):671-680.
[11] Koza JR. (1992). Genetic Programming: On the Programming of Computers by Means of Natural Selection. Vol 1. MIT press.
[12] Rechenberg I, Zurada JM, Marks II RJ, Goldberg C. (1994). Evolution strategy, in computational intelligence: imitating life. Computational intelligence imitating life. IEEE Press, Piscataway. Published online.
[13] Radcliffe NJ, Surry PD. (1994). Formal memetic algorithms. In: AISB workshop on evolutionary computing. Springer; 1-16.
[14] Reynolds RG. (1994). An introduction to cultural algorithms. In: Proceedings of the Third Annual Conference on Evolutionary Programming. World Scientific; 131-139.
[15] Storn R, Price K. (1997). Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of global optimization. 11(4):341-359.
[16] Yao X, Liu Y, Lin G. (1999). Evolutionary programming made faster. IEEE Transactions on Evolutionary computation. 3(2):82-102.
[17] Kim YK, Kim JY, Kim Y. (2000). A coevolutionary algorithm for balancing and sequencing in mixed model assembly lines. Applied intelligence. 13(3):247-258.
[18] Sinha A, Goldberg DE. (2003). A survey of hybrid genetic and evolutionary algorithms. IlliGAL report. 2003004.
[19] Cuevas E, Echavarría A, Ramírez-Ortegón MA. (2014). An optimization algorithm inspired by the States of Matter that improves the balance between exploration and exploitation. Applied intelligence. 40(2):256-272.
[20] Mirjalili S. (2016). SCA: a sine cosine algorithm for solving optimization problems. Knowledge-Based Systems. 96:120-133.
[21] Li S, Chen H, Wang M, Heidari AA, Mirjalili S. (2020). Slime mould algorithm: A new method for stochastic optimization. Future Generation Computer Systems. 111:300-323.
[22] Du H, Wu X, Zhuang J. (2006). Small-world optimization algorithm for function optimization. In: International Conference on Natural Computation. Springer; 264-273.
[23] Formato RA. (2007). Central force optimization: A new metaheuristic with applications in applied electromagnetics. Progress in electromagnetics research. PIER 77, 425–491. Published online.
[24] Tayarani-N M-H, Akbarzadeh-T MR. (2008). Magnetic optimization algorithms a new synthesis. In: 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence). IEEE; 2659-2664.
[25] Rashedi E, Nezamabadi-Pour H, Saryazdi S. (2009). GSA: a gravitational search algorithm. Information sciences (Ny). 179(13):2232-2248.
[26] Kaveh A, Talatahari S. (2010). A novel heuristic optimization method: charged system search. Acta Mechica. 213(3-4):267-289.
[27] Lam AYS, Li VOK. (2010). Chemical-reaction-inspired metaheuristic for optimization. IEEE Transactions on Evolutionary Computation. 14(3):381-399.
[28] Hatamlou A. (2013). Black hole: A new heuristic optimization approach for data clustering. Information sciences. 222:175-184.
[29] Moghaddam FF, Moghaddam RF, Cheriet M. (2012). Curved space optimization: a random search based on general relativity theory. arXiv preprint arXiv:1208.2214. Published online.
[30] Mirjalili S, Mirjalili SM, Hatamlou A. (2016). Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Computing and Applications. 27(2):495-513.
[31] Varaee H, Ghasemi MR. (2017). Engineering optimization based on ideal gas molecular movement algorithm. Engineering with Computers. 33(1):71-93. doi:10.1007/s00366-016-0457-y
[32] Kaveh A, Bakhshpoori T. (2016). Water evaporation optimization: a novel physically inspired optimization algorithm. Computers & Structures. 167:69-85.
[33] Muthiah-Nakarajan V, Noel MM. (2016). Galactic Swarm Optimization: A new global optimization metaheuristic inspired by galactic motion. Applied Soft Computing. 38:771-787.
[34] Kaveh A, Ghazaan MI. (2017). A new meta-heuristic algorithm: vibrating particles system. Scientia Iranica. Transaction A, Civil Engineering. 24(2):551.
[35] Eberhart R, Kennedy J. (1995). A new optimizer using particle swarm theory. In: MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science. Ieee; 39-43.
[36] Li XL. (2003). A new intelligent optimization-artificial fish swarm algorithm. Doctor thesis, Zhejiang University of Zhejiang, China. Published online.
[37] Karaboga D. (2005). An Idea Based on Honey Bee Swarm for Numerical Optimization. Technical report-tr06, Erciyes university, engineering faculty, computer.
[38] Roth M. (2005). Termite: A swarm intelligent routing algorithm for mobile wireless ad-hoc networks. Springer, Published online.
[39] Dorigo M, Birattari M, Stutzle T. (2006). Ant colony optimization. IEEE Studies in Computational Intelligence. 1(4):28-39.
[40] Eusuff M, Lansey K, Pasha F. (2006). Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization. Engineering optimization. 38(2):129-154.
[41] Mucherino A, Seref O. (2007). Monkey search: a novel metaheuristic search for global optimization. In: AIP Conference Proceedings. Vol 953. American Institute of Physics; 162-173.
[42] Yang X-S. (2010). Firefly algorithm, stochastic test functions and design optimisation. International Journal of Bio-Inspired Computation. 2(2):78-84.
[43] Gandomi AH, Alavi AH. (2012). Krill herd: a new bio-inspired optimization algorithm. Communications in nonlinear science and numerical simulation. 17(12):4831-4845.
[44] Gandomi AH, Yang XS, Alavi AH. (2013). Cuckoo search algorithm: A metaheuristic approach to solve structural optimization problems. Engineering with computers. 29(1):17-35.
[45] Mirjalili S. (2015). The ant lion optimizer. Advances in engineering software, 83:80-98.
[46] Mirjalili S. (2016). 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.
[47] Mirjalili S. (2015). Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-Based Systems. 89:228-249.
[48] Mirjalili S, Lewis A. (2016). The whale optimization algorithm. Advances in engineering software. 95:51-67.
[49] Mirjalili S, Gandomi AH, Mirjalili SZ, Saremi S, Faris H, Mirjalili SM. (2017). Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems. Advances in engineering software. 114:163-191.
[50] Heidari AA, Mirjalili S, Faris H, Aljarah I, Mafarja M, Chen H. (2019). Harris hawks optimization: Algorithm and applications. Future generation computer systems. 97:849-872.
[51] Azizyan G, Miarnaeimi F, Rashki M, Shabakhty N. (2019). Flying Squirrel Optimizer (FSO): A novel SI-based optimization algorithm for engineering problems. Iranian Journal of Optimization. 11(2):177-205.
[52] Shadravan S, Naji HR, Bardsiri VK. (2019). The Sailfish Optimizer: A novel nature-inspired metaheuristic algorithm for solving constrained engineering optimization problems. Engineering Applications of Artificial Intelligence. 80:20-34.
[53] Jia H, Peng X, Lang C. (2021). Remora optimization algorithm. Expert Systems with Applications. 185:115665.
[54] Połap D, Woźniak M. (2021). Red fox optimization algorithm. Expert Systems with Applications. 166:114107.
[55] Hama Rashid DN, Rashid TA, Mirjalili S. (2021). ANA: Ant Nesting Algorithm for Optimizing Real-World Problems. Mathematics. 9(23):3111.
[56] MiarNaeimi F, Azizyan G, Rashki M. (2021). Horse herd optimization algorithm: A nature-inspired algorithm for high-dimensional optimization problems. Knowledge-Based Systems. 213:106711.
[57] Trojovský P, Dehghani M. (2022). Pelican Optimization Algorithm: A Novel Nature-Inspired Algorithm for Engineering Applications. Sensors. 22(3):855.
[58] Glover F. (1989). Tabu search—part I. ORSA Journal on computing. 1(3):190-206.
[59] Geem ZW, Kim JH, Loganathan GV. (2001). A new heuristic optimization algorithm: harmony search. Simulation. 76(2):60-68.
[60] Dai C, Zhu Y, Chen W. (2006). Seeker optimization algorithm. In: International Conference on Computational and Information Science. Springer; 167-176.
[61] Atashpaz-Gargari E, Lucas C. (2007). Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In: 2007 IEEE Congress on Evolutionary Computation. Ieee; 4661-4667.
[62] Simon D. (2008). Biogeography-based optimization. IEEE transactions on evolutionary computation. 12(6):702-713.
[63] Kashan AH. (2009). League championship algorithm: a new algorithm for numerical function optimization. In: 2009 International Conference of Soft Computing and Pattern Recognition. IEEE; 43-48.
[64] Tan Y, Zhu Y. (2010). Fireworks algorithm for optimization. In: International Conference in Swarm Intelligence. Springer; 355-364.
[65] Eita MA, Fahmy MM. (2010). Group counseling optimization: a novel approach. In: Research and Development in Intelligent Systems XXVI. Springer; 195-208.
[66] Rao R V, Savsani VJ, Vakharia DP. (2011). Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-Aided Design. 43(3):303-315.
[67] Ramezani F, Lotfi S. (2013). Social-based algorithm (SBA). Applied Soft Computing. 13(5):2837-2856.
[68] Ahmadi S-A. (2017). Human behavior-based optimization: a novel metaheuristic approach to solve complex optimization problems. Neural Computing and Applications. 28(1):233-244.
[69] Shabani A, Asgarian B, Gharebaghi SA, Salido MA, Giret A. (2019). A new optimization algorithm based on search and rescue operations. Mathematical Problems in Engineering.
[70] Chou J-S, Nguyen N-M. (2020). FBI inspired meta-optimization. Applied Soft Computing. 93:106339.
[71] Zeidabadi FA, Dehghani M. (2022). Poa: Puzzle optimization algorithm. International Journal of Intelligent Engineering Systems. 15:273-281.
[72] Veysari EF. (2022). A New Optimization Algorithm Inspired by the Quest for the Evolution of Human Society: Human Felicity Algorithm. Expert Systems with Applications. Published online 116468.
[73] Ferrer L, Pastor A. (2017). The Portuguese man-of-war: Gone with the wind. Regional Studies in Marine Science. 14:53-62.
[74] Abascal AJ, Castanedo S, Mendez FJ, Medina R, Losada IJ. (2009). Calibration of a Lagrangian transport model using drifting buoys deployed during the Prestige oil spill. Journal of Coastal Research. 25(1):80-90.
[75] Dean RG, Dalrymple RA. (1991). Water Wave Mechanics for Engineers and Scientists. Vol 2. world scientific publishing company.
[76] Collins III CO. (2014). Typhoon Generated Surface Gravity Waves Measured by NOMAD-Type Buoys. University of Miami.
[77] You Z-J. (2008). A close approximation of wave dispersion relation for direct calculation of wavelength in any coastal water depth. Applied Ocean Research. 30(2):113-119.
[78] Kobilarov M. (2012). Cross-entropy randomized motion planning. Robot System. Published online 153-160.
[79] Liu Z, Doucet A, Singh SS. (2004). The cross-entropy method for blind multiuser detection. In: Information Theory, 2004. ISIT 2004. Proceedings. International Symposium on. IEEE; 510.
[80] Yang X-S. (2010). Nature-Inspired Metaheuristic Algorithms. Luniver press.
[81] Li LJ, Huang ZB, Liu F. (2009). A heuristic particle swarm optimization method for truss structures with discrete variables. Computers & Structures. 87(7-8):435-443.
[82] Sadollah A, Bahreininejad A, Eskandar H, Hamdi M. (2013). Mine blast algorithm: A new population based algorithm for solving constrained engineering optimization problems. Applied Soft Computing Journal. 13(5):2592-2612. doi:10.1016/j.asoc.2012.11.026
[83] Cheng M-Y, Prayogo D. (2014). Symbiotic organisms search: a new metaheuristic optimization algorithm. Computers & Structures. 139:98-112.
[84] Gross JL. (2001). AISC Design Guide No. 12 Modification of Existing Welded Steel Moment Frame Connections for Seismic Resistance. In: North American Steel Constrction Conference.
