[1] Lv, X. W., Huang, S., Hui, Z. W., & Ji, H. J. (2018). Test cases generation for multiple paths based on PSO algorithm with metamorphic relations. Iet Software, 12(4), 306-317.
[2] Avila-George, H., Torres-Jimenez, J., Gonzalez-Hernandez, L., & Hernández, V. (2013). Metaheuristic approach for constructing functional test‐suites. Iet Software, 7(2), 104-117.
[3] Holland, J. H. (1992). Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT press.
[4] Kirkpatrick, S., Gelatt Jr, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. science, 220(4598), 671-680.
[5] Kennedy, J., & Eberhart, R. (1995, November). Particle swarm optimization. In Proceedings of ICNN'95-international conference on neural networks (Vol. 4, pp. 1942-1948). IEEE.
[6] Geem, Z. W., Kim, J. H., & Loganathan, G. V. (2001). A new heuristic optimization algorithm: harmony search. simulation, 76(2), 60-68.
[7] Colorni, A., Dorigo, M., & Maniezzo, V. (1991, December). Distributed optimization by ant colonies. In Proceedings of the first European conference on artificial life (Vol. 142, pp. 134-142).
[8] Glover, F. (1990). Tabu search—part II. ORSA Journal on computing, 2(1), 4-32.
[9] Yang, X. S. (2010). A new metaheuristic bat-inspired algorithm. Nature inspired cooperative strategies for optimization (NICSO 2010), 65-74.
[10] 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 design, 43(3), 303-315.
[11] Chu, C. W., Lin, M. D., Liu, G. F., & Sung, Y. H. (2008). Application of immune algorithms on solving minimum-cost problem of water distribution network. Mathematical and Computer Modelling, 48(11-12), 1888-1900.
[12] Atashpaz-Gargari, E., & Lucas, C. (2007, September). Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In 2007 IEEE congress on evolutionary computation (pp. 4661-4667). Ieee.
[13] Rashedi, E., Nezamabadi-Pour, H., & Saryazdi, S. (2009). GSA: a gravitational search algorithm. Information sciences, 179(13), 2232-2248.
[14] Naruei, I., & Keynia, F. (2021). A new optimization method based on COOT bird natural life model. Expert Systems with Applications, 183, 115352.
[15] Askarzadeh, A. (2016). A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Computers & structures, 169, 1-12.
[16] Houssein, E. H., Saad, M. R., Hashim, F. A., Shaban, H., & Hassaballah, M. (2020). Lévy flight distribution: A new metaheuristic algorithm for solving engineering optimization problems. Engineering Applications of Artificial Intelligence, 94, 103731.
[17] Simon, D. (2008). Biogeography-based optimization. IEEE transactions on evolutionary computation, 12(6), 702-713.
[18] Punnathanam, V., & Kotecha, P. (2016). Yin-Yang-pair Optimization: A novel lightweight optimization algorithm. Engineering Applications of Artificial Intelligence, 54, 62-79.
[19] He, S., Wu, Q. H., & Saunders, J. R. (2009). Group search optimizer: an optimization algorithm inspired by animal searching behavior. IEEE transactions on evolutionary computation, 13(5), 973-990.
[20] Moghdani, R., Elaziz, M. A., Mohammadi, D., & Neggaz, N. (2021). An improved volleyball premier league algorithm based on sine cosine algorithm for global optimization problem. Engineering with Computers, 37, 2633-2662.
[21] Hasanzadeh, M. R., Keynia, F., & Hashemipour, M. (2022). A new index‐based hyper‐heuristic algorithm for global optimisation problems. IET Software, 16(5), 493-515.
[22] Yang, X. S. (2010). Firefly algorithm, stochastic test functions and design optimisation. International journal of bio-inspired computation, 2(2), 78-84.
[23] Zhao, S., Zhang, T., Ma, S., & Chen, M. (2022). Dandelion Optimizer: A nature-inspired metaheuristic algorithm for engineering applications. Engineering Applications of Artificial Intelligence, 114, 105075.
[24] Sulaiman, M. H., Mustaffa, Z., Saari, M. M., & Daniyal, H. (2020). Barnacles mating optimizer: A new bio-inspired algorithm for solving engineering optimization problems. Engineering Applications of Artificial Intelligence, 87, 103330.
[25] Wang, T. L., Gu, S. W., Liu, R. J., Chen, L. Q., Wang, Z., & Zeng, Z. Q. (2025). Cuckoo catfish optimizer: a new meta-heuristic optimization algorithm. Artificial Intelligence Review, 58(10), 326.
[26] Agushaka, J. O., Ezugwu, A. E., & Abualigah, L. (2023). Gazelle optimization algorithm: a novel nature-inspired metaheuristic optimizer. Neural Computing and Applications, 35(5), 4099-4131.
[27] Lang, Y., & Gao, Y. (2025). Dream Optimization Algorithm (DOA): A novel metaheuristic optimization algorithm inspired by human dreams and its applications to real-world engineering problems. Computer Methods in Applied Mechanics and Engineering, 436, 117718.
[28] Abdel-Basset, M., Mohamed, R., Jameel, M., & Abouhawwash, M. (2023). Spider wasp optimizer: A novel meta-heuristic optimization algorithm. Artificial Intelligence Review, 56(10), 11675-11738.
[29] Mohammed, B. O., Aghdasi, H. S., & Salehpour, P. (2025). Dhole optimization algorithm: A new metaheuristic algorithm for solving optimization problems. Cluster Computing, 28(7), 430.
[30] Memarzadeh, G., & Keynia, F. (2021). A new optimal energy storage system model for wind power producers based on long short term memory and Coot Bird Search Algorithm. Journal of Energy Storage, 44, 103401.
[31] Memarzadeh, G., & Keynia, F. (2020). A new index‐based method for optimal DG placement in distribution networks. Engineering Reports, 2(10), e12243.
[32] Nguyen, T. P., Tran, T. T., & Vo, D. N. (2019). Improved stochastic fractal search algorithm with chaos for optimal determination of location, size, and quantity of distributed generators in distribution systems. Neural Computing and Applications, 31, 7707-7732.
[33] Injeti, S. K., & Kumar, N. P. (2013). A novel approach to identify optimal access point and capacity of multiple DGs in a small, medium and large scale radial distribution systems. International Journal of Electrical Power & Energy Systems, 45(1), 142-151.
[34] Sultana, S., & Roy, P. K. (2016). Krill herd algorithm for optimal location of distributed generator in radial distribution system. Applied Soft Computing, 40, 391-404.
[35] Sultana, S., & Roy, P. K. (2014). Multi-objective quasi-oppositional teaching learning based optimization for optimal location of distributed generator in radial distribution systems. International Journal of Electrical Power & Energy Systems, 63, 534-545.
[36] Moradi, M. H., & Abedini, M. (2012). A combination of genetic algorithm and particle swarm optimization for optimal DG location and sizing in distribution systems. International Journal of Electrical Power & Energy Systems, 34(1), 66-74.
