###### Abstract
Examples of various metaheuristic algorithms are particle swarm optimization (PSO), the moth-swarm algorithm (MSA), whale optimization algorithm (WOA), grey wolf optimization (GWO), and elephant herding optimization (EHO) etc. Physics based algorithms that are stimulated from different nature/physics laws and swarm-based algorithms which are inspired from the social manner of a cluster of animals have been included in the group of metaheuristic algorithms. The Gravitational Search Algorithm (GSA) has been suggested by [PERSON] et al. in [17], that imitates the concept of [PERSON]'s gravity law. [PERSON] et al., in [18], developed a Multi-Verse Optimization (MVO) algorithm which mimics the idea of cosmology that is comprised of wormholes, white holes and black holes. Physics based algorithms that have been utilized for the solution of OPF problems are GSA, presented by researchers in [19; 20]. The Electromagnetism-like Mechanism Method (EMM) presented by the authors in [21] and [PERSON] et al. in [22] used the Doubly Modified logarithmic Barrier Function (M2 BF) algorithm. Other swarm-based algorithms that have been utilized for OPF solutions exist such as PSO, presented by [PERSON] and [PERSON] in [23], Moth-flame Optimization (MFO) [24], Ant Lion Optimization (ALO) [25], GWO [26], and the Grasshopper Optimization Algorithm (GOA) [27]. The authors in [28; 29; 30] used PSO and its enhanced variants to provide an optimal solution for OPF problems. A novel meta-heuristic moth swarm algorithm (MSA) has been used for OPF solutions, which is inspired by the path finding processes of moths [31]. The performance of the moth swarm algorithm has been improved with arithmetic crossover (MSA-AC) and has been successfully utilized in the optimization of OPF problems for HVDC systems [32]. The results obtained show the better exploration and speedy convergence capability of MSA-AC for a global optimum. A modified sine cosine algorithm (MSCA) has been used for OPF solutions by the researcher in [33]. The effectiveness of modified MSCA has been verified in terms of a decrease in computational time and improvement of finding global OPF solutions with different objective functions. In reference [34], a novel improved artificial bee colony (IABC) optimization algorithm hasbeen used for OPF solutions. The poor exploitation capability of the original ABC technique has been improved by an orthogonal learning approach, which guesses the optimum solution among the solution vectors created on restricted trials rather than a complete search. The dominance of IABC has been verified on IEEE 30 and 118 bus test systems, while optimizing the OPF solution, where IABC achieves a lower cost in comparison to other heuristics techniques for all test cases. To improve the global searching ability, hybridization of two metaheuristic algorithms has been conducted to obtain the global optimum results; a hybrid of dragon fly and PSO (DA-PSO) have been proposed in reference [35] in which the exploitation and exploration capability of both algorithms are enhanced. The competence of the proposed algorithm (DA-PSO) has been confirmed on IEEE 30 and 57 bus test systems, while taking into account multi-objective and single-objective cases of power losses, cost and emissions. The dominance of the suggested algorithm has been proven by simulation results. However, computation time is increased due to the sequential operation of the hybrid technique. The authors in [36] present a hybrid DE along with a harmony search algorithm (Hybrid DE-HS) for OPF solutions. The usefulness of the applied hybrid DE-HS algorithm has been verified on IEEE 30, 118 and 300 bus test systems with reactive power compensation and transformer tap settings as a control variable. Other hybrid algorithms utilized for OPF solutions are hybrid PSO-GSA [37], imperialist competitive algorithm (ICA) hybridized with sequential quadratic programming by [PERSON] et al.