## 1 Introduction  
With the advancement of industry and growth in society, modern power systems have turned out to be very complex. Sustainability and reliability in electric power networks can only be achieved by optimizing the variables of control so that OPF objectives are achieved. The control parameters cover generation, transmission, and distribution systems, and their direct influence is on system stability, economic dispatch, and efficient power flow [1; 2; 3; 4; 5]. In the generation sector, active and reactive power output and voltage set points are controlled to meet demand requirements at minimum voltage levels by means of minimum operational cost. Transmission line control is enforced with transformer tap settings, phase-shifting transformers and FACTS devices for voltage stability and power flow control [3; 6; 7; 8]. On the other side, distribution networks perform load shedding and apply voltage regulators and capacitor banks to provide voltage control and reactive power management [9; 10; 11; 12]. While each control variable can be optimized individually, their interaction is very critical to ensure power balance and efficient power flow [13; 14]. On the other hand, solving power flow equations alone cannot provide the solution to achieve economic as well as reliable system operation [15; 16]. OPF plays a critical role in optimizing power system control parameters to ensure efficient operations. In general, a single objective could be minimum voltage drop, minimum active power losses, or minimum fuel cost, while multi-objective optimization could include more than one among those stated above. The linear and nonlinear programming methods along with mixed-integer programming are considered classic versions of OPF techniques that failed in handling the highly complicated large-scale nonconvex problems. Therefore, metaheuristic algorithms became popular owing to their flexible and powerful techniques in solving optimization problems [17; 18; 19]. Among these, PSO is widely applied for power system optimization due to its simplicity and fast convergence. Inspired by the bird flocking and schooling of fish, iteratively, PSO adjusts solutions concerning both individual and group experiences [20; 21; 22; 23; 24]. The other most used technique is genetic algorithms, which are based on the principles of natural selection to avoid local optima and have been very effective in hard optimization environments [25; 26; 27]. Recent developments in metaheuristics have also proposed hybrid systems that combine heuristic methods with nature-inspired algorithms. Examples include powerful and flexible optimization solutions such as the Dragonfly Algorithm, proposed based on dragonfly swarming behavior [28], or the Ant Lion Optimizer, which acts based on ant lion hunting strategies [29]. Similarly, GWO and HHO have taken inspiration from the social hierarchy in wolves and cooperative behaviors in hawks [30; 31], while MVO has integrated several concepts borrowed from physics into its algorithm [32; 33]. Modified multi-objective variants of ALO and GWO have been developed to handle complex multi-objective OPF problems [34; 35]. Other popular algorithms are MFO, inspired by the navigation mechanisms of moths showing excellent performance in local optimum escaping [36], GOA, inspired by modeling the swarming of grasshoppers, and HO, inspired by the territorial walking style of hippocampus [37; 38; 39]. WOA, proposed with inspiration from whales' bubble-net hunting strategy, has become one of the most well-known algorithms because of its simplicity and robustness against different optimization problems [40]. Other methods include, among many others, BBO [41], the hybrid PSO-Gravitational Search Optimization [42; 43; 44], and many other variants continuously in evolution, with proof of efficiency in a wide application range [45; 46; 47; 48; 49; 50; 51; 52]. Although a few strategies are at hand, some of them still face difficulties while finding the optimal combination of control variables for OPF.