###### Abstract
To overcome these drawbacks, meta-heuristic optimization methods based on intelligent soft computing and evolutionary algorithms have been developed, especially in recent decades, such as Genetic Algorithms (GAs) [7], Particle Swarm Optimization (PSO) [8], Ant Colony Optimization (ACO) [9], Artificial Bee Colony (ABC) [10], Gravitational Search Algorithm (GSA) [11], Grey Wolf Optimizer (GWO) [12], Dragonfly Algorithm (DA) [13], Teaching-Learning-based Optimization Algorithm (TLBO) [14], Firefly Algorithm (FFA) [15], Harris Hawks Optimization [16], Turbulent Flow of Water-Based Optimization (TFWO) [17], and so on. They have been successfully applied to solve MINLP-OPF problems without considering the derivability and continuity of the objective functions and constraints. The two main phases of a meta-heuristic algorithm that determine its performance in achieving a global optimal solution are exploration and exploitation [18]. In the past decade, various strategies have been developed to enhance the effectiveness of meta-heuristic techniques by balancing these two phases, particularly when the technique shows weaknesses in one of them. The first strategy focuses on improving the basic meta-heuristic optimization algorithm by adjusting the parameters of the search equation that guide exploration, exploitation, or both. In [19], a Modified Sine Cosine Algorithm (MSCA) is introduced to address the issue of premature convergence in the basic SCA, with two key modifications highlighted. The first modification involves replacing the existing linear update mechanism in the standard SCA with a new nonlinear update mechanism. The second modification adjusts the updated design variable of each agent to include an average design variable between the current design variable of each agent and the best current design variable. In [20], the author proposes an improved Whale Optimization Algorithm (WOA) based on a Nonlinear Adaptive Weight and Golden Sine Operator (NGS-WOA). First, NGS-WOA introduces a nonlinear adaptive weight, allowing search agents to adaptively explore the search space and balance the exploration and exploitation phases. Second, the improved Golden Sine Operator is integrated into the WOA. The second strategy for improving the performance of meta-heuristics involves combining or hybridizing two meta-heuristic techniques to leverage their strengths in managing both the exploration and exploitation phases [18]. To improve the solution quality and speed up the convergence characteristics of meta-heuristics-based OPF problems, various strategies using hybrid meta-heuristic techniques have been proposed in the literature. In [21], a hybrid PSO and Pattern Search (PS) algorithm named PSO-PS is considered to improve the OPF solution by merging the advantages of both basic algorithms with the inclusion of FACTS devices. In [22], a combination of Differential Evolution and Harmony Search algorithms (DE-HS) is elaborated to improve the OPF solution by considering various scales of test systems. In [23], the authors proposed a hybrid meta-heuristic optimization approach based on the Dragonfly Algorithm (DA) and PSO to enhance the search capability to achieve the best global solution quality for solving the single- and multi-objective OPF problems. As reported in [24], a new hybrid approach using the Firefly and PSO algorithms (HFPSO) has been applied to the OPF solution to enhance the exploration and exploitation mechanisms with fast convergence to deal with various complexities of the OPF problem. The recently published work in [25] presents a novel hybrid optimization technique combining ABC and Salp Swarm Algorithms (SSAs) named ABC-SSA to solve the Optimal Reactive Power Dispatch (ORPD) as a sub-problem of OPF. The efficiency of ABC-SSA in balancing between exploration and exploitation mechanisms has been proven for the large-scale test system IEEE 30-bus.