## 1 Introduction  
The increase on load demand and the global concerns about the environment as well as the enormous technologies for connecting renewable energy resources (RES) all played an important role in the distribution networks planning. DGs have considerable effects on the electric network design, construction, analysis, operation, and maintenance [1]. A comprehensive survey and analysis must be done before DG integration to ensure the judicious and efficient use of energy and hence maximize the benefits [2]. Location, capacity, and power factor of the DGs are the three main factors that affect the power system network. These three factors have significant impact on the protection system, reliability, stability, power losses, voltage profile, and power quality [3; 4; 5; 6; 7]. Recent studies and optimization techniques have been employed to find the optimal location, capacity, and power factor of DGs being added to the network to improve the network characteristics; one of the most important characteristic is the power losses. For many utilities, reducing these losses has become one of the significant challenges. DG units may be used in distribution networks for this goal. This is achieved by placing the DG in a proper location with a proper capacity and proper power factor to provide minimum power losses. These proper values of the DG parameters that minimize the total power losses in the distribution networks are referred to in this paper as optimal parameters of the interconnected DG unit. As shown in [6; 7; 8; 9; 10; 11], the variation of the DG settings to a condition beyond the optimal parameters, decreases the power losses in the network and then it rapidly increases them again. DG planning has attracted the interest of many researchers and numerous publications can be found on this subject [8; 9; 10; 11]. Several methods have recently been proposed in order to minimize the power losses in the DG-integrated distribution networks. These methods can be classified into three main categories: (1) numerical-based category, (2) heuristic-based category, (3) analytical-based category [12]. Regarding the numerical-based category, there are many algorithms proposed and the most common algorithms are exhaustive search, gradient search, OPF (optimal power flow), and linear programming [13; 14; 15; 16; 17]. These algorithms have demonstrated to be effective for obtaining the optimal DG capacities at certain locations or the optimal DG location for a given DG capacity. Therefore, these algorithms did not succeed in accurately representing an optimization problem aiming at simultaneously optimizing DG capacity and location. On the other hand, heuristic-based category methods are based on employing advanced computational intelligence algorithms, such as genetic algorithms [18; 19], particle swarm optimization algorithms [20], harmony search algorithms [21], tabu search algorithms [22], and Dragonfly algorithm [23]. It is valuable to mention that the main feature of these techniques is that they can provide near-optimal solutions thanks to their computational robustness but, at the same time, they need intensive computational capabilities. Analytical-based methods attract active research interests, as they are easy to implement and fast. Analytical-based methods often follow various techniques to simplify the optimization problem. Within this category, different techniques and methodologies have been developed in order to achieve maximum possible losses reduction. Some of these methods were concerned with installing a single DG unit such as a method in [24], which is based on power stability index (PSI) and the method in [25], which is based on power loss sensitivity (PLS) index. When these methods were applied for multiple DG units, they were efficient only when installing the DG units sequentially one-by-one. Moreover, in [26], the optimum location-capacity pair of a single DG unit was obtained using an analytical approach.