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Recent metaheuristics on control parameter determination

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                system using Bat algorithm optimized PD–PID       timizer for engineering applications. Expert Sys-
                cascade controller. International Journal of Elec-  tems with Applications, 198, 116924.
                trical Power & Energy Systems, 68, 364-372.   [31] Agushaka, J. O., Ezugwu, A. E., & Abualigah, L.
            [19] Puangdownreong, D., Nawikavatan, A., & Tham-     (2023). Gazelle optimization algorithm: a novel
                marat, C. (2016). Optimal design of I-PD con-     nature-inspired metaheuristic optimizer. Neural
                troller for DC motor speed control system by      Computing and Applications, 35(5), 4099-4131.
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                83-86.                                            optimization algorithm: A novel nature-inspired
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                controller design for power system frequency con-  [33] Askarzadeh, A. (2016). A novel metaheuristic
                trol. Sustainable Energy, Grids and Networks, 16,  method for solving constrained engineering opti-
                278-299.                                          mization problems: crow search algorithm. Com-
            [21] Wang, L., Ni, H., Zhou, W., Pardalos, P. M.,     puters & structures, 169, 1-12.
                Fang, J., & Fei, M. (2014). MBPOA-based LQR   [34] Mirjalili, S., & Lewis, A. (2016). The whale opti-
                controller and its application to the double-     mization algorithm. Advances in engineering soft-
                parallel inverted pendulum system. Engineering    ware, 95, 51-67.
                Applications of Artificial Intelligence, 36, 262-  [35] Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014).
                268.                                              Grey wolf optimizer. Advances in engineering
            [22] Demirta¸s, M., & Ahmad, F. (2023). Fractional    software, 69, 46-61.
                fuzzy PI controller using particle swarm opti-  [36] Yang, X. S. (2012, September). Flower pollina-
                mization to improve power factor by boost con-    tion algorithm for global optimization. In Inter-
                verter. An International Journal of Optimization  national conference on unconventional computing
                and Control: Theories & Applications (IJOCTA).    and natural computation (pp. 240-249). Berlin,
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            [23] Ozyetkin, M. M., & Birdane, H. (2023). The pro-  Heidelberg: Springer Berlin Heidelberg.
                cesses with fractional order delay and PI con-  [37] Hansen, N. (2016). The CMA evolution strategy:
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                An International Journal of Optimization and  [38] Olmez, Y., Koca, G. O., & Akpolat, Z. H. (2022).
                Control:  Theories & Applications (IJOCTA),       Clonal selection algorithm based control for two-
                13(1), 81-91.                                     wheeled self-balancing mobile robot. Simulation
            [24] Askari, Q., Younas, I., & Saeed, M. (2020). Po-  Modelling Practice and Theory, 118, 102552.
                litical Optimizer: A novel socio-inspired meta-  [39] Sharma, A., & Singh, N. (2024). Load fre-
                heuristic for global optimization. Knowledge-     quency control of connected multi-area multi-
                based systems, 195, 105709.                       source power systems using energy storage and
            [25] Faramarzi, A., Heidarinejad, M., Stephens, B.,   lyrebird optimization algorithm tuned PID con-
                & Mirjalili, S. (2020). Equilibrium optimizer: A  troller. Journal of Energy Storage, 100, 113609.
                novel optimization algorithm. Knowledge-based  [40] Ozmen Koca, G., & Korkmaz, D. (2019). Neural
                systems, 191, 105190.                             network based control of a two-mass drive system.
            [26] Abualigah, L., Yousri, D., Abd Elaziz, M., Ewees,  International Journal of Intelligent Systems and
                A. A., Al-Qaness, M. A., & Gandomi, A. H.         Applications in Engineering, 7(2), 92-98.
                (2021). Aquila optimizer: a novel meta-heuristic
                optimization algorithm. Computers & Industrial
                Engineering, 157, 107250.                     Yagmur Olmez earned her M.Sc. and Ph.D. degrees
            [27] Karami, H., Anaraki, M. V., Farzin, S., & Mir-  in Mechatronics Engineering from the Institute of Sci-
                jalili, S. (2021). Flow direction algorithm (FDA):  ence at Firat University, Elazig, Turkiye, in 2018 and
                a novel optimization approach for solving opti-  2024, respectively. Currently, she is a Research Assis-
                mization problems. Computers & Industrial En-  tant in the Department of Mechatronics Engineering
                gineering, 156, 107224.                       at the Faculty of Engineering, Nigde Omer Halisdemir
            [28] Akbari, M. A., Zare, M., Azizipanah-Abarghooee,  University. Her research interests encompass image
                R., Mirjalili, S., & Deriche, M. (2022). The chee-  processing, metaheuristic algorithms, artificial intel-
                tah optimizer: A nature-inspired metaheuristic  ligence, system modeling, and advanced control tech-
                algorithm for large-scale optimization problems.  niques.
                Scientific reports, 12(1), 10953.                https://orcid.org/0000-0002-1615-7390
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