[1] E. G. Talbi, “Metaheuristics: from design to implementation”. John Wiley & Sons, vol. 205, no. 2, pp. 486-487, 2009.
[2] I. Fister, Jr. Fister, X. S. Yang, & J. Brest, “A comprehensive review of firefly algorithms”. Swarm and Evolutionary Computation, vol. 13, pp. 34-46, 2013.
[3] X. S. Yang, and X. He, “Bat algorithm: literature review and applications”. International Journal of Bio-Inspired Computation, vol. 5, no. 3, pp. 141-149, 2013.
[4] M. Abdel-Basset, and L. A. Shawky, “Flower pollination algorithm: a comprehensive review”. Artificial Intelligence Review, vol. 52, pp. 2533-2557, 2019.
[5] A. Lambora, K. Gupta, and K. Chopra, “Genetic algorithm-A literature review”. In 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon), pp. 380-384. IEEE. 2019, February.
[6] J. Kennedy, and R. Eberhart, “Particle swarm optimization”. In Proceedings of ICNN'95-International Conference on Neural Networks, vol. 4, pp. 1942-1948), IEEE, 1995, November.
[7] M. Dorigo, M. Birattari, and T. Stutzle, “Ant colony optimization”. IEEE Computational Intelligence Magazine, vol. 1, no. 4, pp. 28-39, 2006.
[8] D. Bertsimas, and J. Tsitsiklis, “Simulated annealing”. Statistical Science, vol. 8, no. 1, pp. 10-15, 1993.
[9] F. S. Gharehchopogh, “Quantum-inspired metaheuristic algorithms: comprehensive survey and classification”. Artificial Intelligence Review, vol. 56, no. 6, pp. 5479-5543, 2023.
[10] T. Bartz‐Beielstein, J. Branke, J. Mehnen, and O. Mersmann, “Evolutionary algorithms”. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 4, no. 3, pp. 178-195, 2014.
[11] K. V. Price, “Differential evolution”. In Handbook of Optimization: From Classical to Modern Approach, pp. 187-214. Springer Berlin Heidelberg, 2013.
[12] S. Chinnasamy, M. Ramachandran, M. Amudha, and K. Ramu, “A review on hill climbing optimization methodology”. Recent Trends in Management and Commerce, vol. 3, no. 1, pp. 1-7, 2022.
[13] F. Glover, and M. Laguna, “Tabu search”. In: Du, DZ., Pardalos, P.M. (eds) Handbook of Combinatorial Optimization. Springer Boston MA, 1998.
[14] M. J. Willis, H. G. Hiden, P. Marenbach, B. McKay, and G. A. Montague, “Genetic programming: An introduction and survey of applications”. In Second International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications, pp. 314-319. IET, 1997, September.
[15] H. R. Lourenço, O. C. Martin, and T. Stützle, “Iterated local search”. In Handbook of Metaheuristics, pp. 320-353. Boston, MA: Springer US, 2003.
[16] D. Jungnickel, and D. Jungnickel, “The greedy algorithm”. Graphs, Networks and Algorithms, vol. 3, pp. 129-153, 1999.
[17] M. Mahmood, and B. Al-Khateeb, “The blue monkey: A new nature inspired metaheuristic optimization algorithm”. Periodicals of Engineering and Natural Sciences, vol. 7, no. 3, pp. 1054-1066, 2019.
[18] M. Dehghani, P. Trojovský, and O. P. Malik, “Green anaconda optimization: a new bio-inspired metaheuristic algorithm for solving optimization problems”. Biomimetics, vol. 8, no. 1, pp. 121, 2023.
[19] A. Askarzadeh, “A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm”. Computers & Structures, vol. 169, pp. 1-12, 2016.
[20] G. I. Sayed, A. Darwish, and A. E. Hassanien, “Quantum multiverse optimization algorithm for optimization problems”. Neural Computing and Applications, vol. 31, pp. 2763-2780, 2019.
[21] V. Hayyolalam, and A. A. P. Kazem, “Black widow optimization algorithm: a novel meta-heuristic approach for solving engineering optimization problems”. Engineering Applications of Artificial Intelligence, vol. 87, pp. 103249, 2020.
[22] S. Mirjalili, “Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm”. Knowledge-Based Systems, vol. 89, pp. 228-249, 2015.
[23] R. V. Rao, V. J. Savsani, and D. P. Vakharia, “Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems”. Computer-Aided Design, vol. 43, no. 3, pp. 303-315, 2011.
[24] S. Mirjalili, “SCA: a sine cosine algorithm for solving optimization problems”. Knowledge-Based Systems, vol. 96, pp. 120-133, 2016.
[25] S. J. Mousavirad, and H. Ebrahimpour-Komleh, “Human mental search: a new population-based metaheuristic optimization algorithm”. Applied Intelligence, vol. 47, pp. 850-887, 2017.
[26] B. Javidy, A. Hatamlou, and S. Mirjalili, “Ions motion algorithm for solving optimization problems”. Applied Soft Computing, vol. 32, pp. 72-79, 2015.
[27] A. Hadian, M. Bagherian, and B. Fathi Vajargah. “A Heuristic Algorithm for Multi-layer Network Optimization in Cloud Computing”. Journal of AI and Data Mining, vol. 9, no. 3, pp. 361-367, 2021.
[28] M. Yazdani, and F. Jolai, “Lion optimization algorithm (LOA): a nature-inspired metaheuristic algorithm”. Journal of Computational Design and Engineering, vol. 3, no. 1, pp. 24-36, 2016.