In general, backward and forward sweep (BS/FS) are primarily used to calculate the power flow in RDPGs. However, the optimal sizes and locations of such SCs, DGs, WTs, or PVG are mainly determined and evaluated by meta-heuristic algorithms. Particularly, the authors in [22] applied the modified slime mold algorithm (MSMA) to determine the optimal sizes and positions of photovoltaic generators and shunt capacitors (SCs) in three different RDPGs with 33, 69 and 85 nodes, for power loss reductions. In addition, the results achieved by MSMA are compared to other meta-heuristic algorithms to validate the real effectiveness of the proposed algorithm, including the Equilibrium Optimizer (EO) and the original version of the slime mold algorithm (SMA). Next, the study [23] employed ant lion optimization (ALO) to find out the best sizes and positions of distributed generators (DGs), to place various configurations of RDPGs from 15 to 85 nodes. Additionally, the study focused on discussing and analyzing the effect of DGs on the grid in several aspects, such as short circuit level, load loss variation, reliability, and voltage profile improvement. In [24], a modified version of particle swarm optimization (PSO) called adaptive particle swarm optimization (APSO) was used to optimize the allocation of DGs-based renewable energy for reaching the minimum value of a multi-objective function. In [25], the moth swarm algorithm (MSA) was executed to achieve the maximum penetration level of both PGs and SCs and reduce the reliance on conventional generating sources. The results obtained by MSA were then compared to two others and indicated the high capability of the applied method. In [26], the Coot optimization algorithm (COOA) and five other meta-heuristic algorithms, including the Archimedes optimization algorithm (AOA), the transient search optimization algorithm (TSOA), the crystal structure algorithm (CrSA), the war strategy optimization algorithm (WSA), and the average and subtraction-based optimizer (ASBO), were executed to optimize the sizes and location of PGs to IEEE 33-node RDPG and 69-node RDPG. The authors have conducted various scenarios of placing PGs on the grid for active power loss reduction. The results pointed out that COOA completely outperforms others and is recognized as the most effective search method. The study [27] applied the harmony search algorithm (HSA) to solve the network reconfiguration problem with the main objective of reaching the minimum value of active power loss and improving the voltage profile in IEEE 33- and IEEE 69-node RDPG. Then, the authors in [28] applied a new algorithm called the pathfinder algorithm (FPA) and successfully determined the best placements of DGs to reconfigure the 18-bus RDPG to reach the best value of energy loss. In [29], both wind and solar energy were considered to be placed in the RDPG to solve the optimal reconfiguration problem using the matrix moth-flame algorithm (MMFA). Improving stability and reliability was also the primary focus of the study. In [30; 31; 32], the crow search algorithm (CrOA), the improved human learning optimization algorithm (IHLOA), and the improved equilibrium optimization algorithm (IEOA) were also employed to optimize the allocation of the renewable energy systems to different RDPG. However, the most striking feature of these studies is considering the uncertainties of renewable-based DGs, which makes the considered problem even more complicated. By fully acknowledging the practical benefit of placing the wind turbines in the distribution power system, this research also provides a strategy to reduce the overall active power loss in the IEEE 85-bus distribution power system. In execution, two novel meta-heuristic algorithms, including the osprey optimization algorithm (OOA) [33] and the walrus optimization algorithm (WOA) [34], are applied to optimize the size and location of the wind turbines in the given grid. Both the OOA and WOA are nature-inspired optimization algorithms.