## 1 Introduction  
The importance of the optimal operation of radial distribution systems (RDSs) arises from the continuous need of highly reliable operation of those systems to ensure high quality delivered-power to the end consumers, which is not an easy mission due to multiple reasons. The main reason is the lack of controllability because of the absence of power generation, in other words, the passive nature of RDSs. Other reasons are the high R/X ratio and the increase of load demand [1]. The operation of RDSs with the presence of those difficulties may lead to many operational problems, such as low reliability and bad quality of electricity, an increase of the system's power losses, high voltage deviation, and poor voltage stability. Converting the nature of RDSs from passive to active by installing small distributed generators (DG) near to end consumers is one of the solutions to overcome those technical problems. DG units improve the voltages along the feeder; enhance the reliability, as well as the quality; increase the voltage stability; allow more power to be transmittedthrough the feeders which defers the investments on future expansion of transmission and distribution systems; and reduce the total system's losses along with their costs [2,3]. In order to extract the maximum advantages from the installed DGs, it is necessary to allocate them at the optimum buses with the optimal sizes, whereas inappropriate installation of DGs could lead to unwanted effects, such as higher power losses. Hence, the optimal planning of DGs (OPDG) is regarded as a critical problem needed to be effectively solved, especially for large-scale RDSs. It is difficult to find the global optimal solution of the OPDG problem, due to the nonlinear nature of the objective functions and constraints. Therefore, this challenge gives researchers a good motivation to develop new algorithms for solving this problem. In recent years, numerous research works using different methods have been published about the OPDG problem in RDSs. Those methods can be basically categorized into groups which are mainly: Analytical techniques, conventional approaches, and evolutionary algorithms [4,5]. Many analytical techniques have been applied for solving the OPDG problem in RDSs [6, 7, 8, 9, 10]. Some of them are based on sensitivity indices, such as power stability index [6], voltage stability index [7], and combined power loss sensitivity [8]. A comparison of optimal DG allocation methods based on many sensitivity approaches was presented in [9]. In [10], the proposed method was based on minimizing the losses associated with the changes in active and reactive components of branch currents caused by DG placements. In general, most of those analytical methods were applied to only minimize power loss in small and medium-scale RDSs. The nonlinear features of the OPDG problem make it solvable by implementing the conventional iterative methods. Two of those methods are: Non-linear programming (NLP) [11], and dynamic programming (DP) [12]. In [13], the active power loss was minimized by a mixed integer non-linear programming (MINLP) based approach, which was used for allocating single and multiple DGs in RDSs. The main disadvantage of those methods was their inability when dealing with complex and large-scale problems, regarding either the computational time or convergence. In the last few decades, evolutionary algorithms (EA) have been massively employed for solving the OPDG problem. Those metaheuristic methodologies have proved their ability to effectively solve that problem even for complex and large-scale RDSs. In [14], the OPDG was addressed as multi-objective mixed integer problem solved by a genetic algorithm (GA) used to minimize the costs of different parts of the system. Many versions of GAs were developed to deal with the OPDG problem, as in [15,16], where the power loss and voltage deviation were minimized by the optimal allocation of DGs, considering uncertainties of load and generation in [15].