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I recent years, the continuous development of electrical loads, especially due to industrial plants and human activities, results in increased number of new transmission lines, power plants, distribution networks and interconnection between different power systems. This effect leads to higher currents and power losses accompanied by voltage drop. Distribution system (DS) is an essential part of this power system problem as it connects loads to the transmission line at substations. About 70% of the power system losses are occurring at distribution system [1]. Therefore, the reduction of the losses in DS is the main concern nowadays. Hence, the world directed to use new generation sources of renewable energy resources (RERs) such as photovoltaic (PV), wind turbines and biomass energy, which are considered economically for supplying energy to electrical grids and suitable for power generation in remote areas [2], [3]. There are many potential benefits of DGs depending on their size and location. Normally, the real power loss and the voltage profile are the base objectives. Some other technical parameters may accompany this base objective such as reactive power requirement, reliability and efficiency of distribution network, emission, load-ability, voltage stability, DG capacity maximization, or economy oriented objectives [4]. There are different types of DG units, which can be classified based on whether they generate or absorb reactive power along with active power generation to (a) type A-DG units or P-type, which produces active power only such as PV (b) type B-DG units or Q-type, which produces reactive power only, like capacitor banks (c) type C-DG units or PQ+-type, which produces active and reactive power like synchronous generators (d) Type D-DG units or PQ--type which produces active power and consumes reactive power, like wind power induction generators. The random placement of DGs and capacitors in DS can cause more voltage drop and higher power losses than losses without them [5], [6]. Therefore, determining the proper placement and capacity of DGs in DS becomes a crucial task for obtaining their maximum possible advantages. Several techniques have been proposed in recent years to determine the optimal locations and sizes of DGs in DS such as Ref. [7], which discussed the adaptive protection using neural networks for high penetration of DGs, but this technique takes very long training time. Ref. [8] made a very hard work to get the effective signals for optimal ratings of RERs as the objective function and Genetic-Moth Swarm Algorithm for Optimal Placement and Capacity of Renewable DG Sources in Distribution Systems