## 1 Introduction
The same objectives of power loss and voltage deviation were minimized by optimally allocating DGs and on-load tap changer (OLTC) in [16]. A particle swarm optimization (PSO) algorithm was also widely used for solving the OPDG problem. A multi-objective index-based method for the optimal planning of multiple DGs in RDSs with different loads was proposed in [17] by applying PSO. In [18], different types of DGs were considered for the optimal planning by minimizing the power loss using PSO. A new formulation was proposed to consider the uncertainties in the renewable DGs output in order to optimally allocate them while minimizing the total harmonic distortion, power loss, and total costs by PSO in [19]. An artificial bee colony (ABC) based method was presented in [20] to solve the OPDG problem by minimizing the power loss. Several algorithms were also developed to solve that problem, such as the modified honey bee mating optimization algorithm (HBMO) [21], the improved gravitational search algorithm (IGSA) [22], and symbiotic organisms search (SOS) algorithm [23]. Moreover, the hybridization of two or more EA methods in order to improve their performances has been rapidly adopted as an efficient way to solve the OPDG problem. The authors in [24] presented a hybrid GA-PSO method to simultaneously minimize the power loss and voltage deviation while enhancing the voltage stability. A multi-objective hybrid teaching--learning based optimization--Grey-Wolf optimizer (MOHTLBOGWO) based on a fuzzy decision-making method was developed in [25] for loss minimization and reliability enhancement using renewable DGs. Another multi-objective opposition based chaotic differential evolution (MOCDE) algorithm was proposedin [26] for solving a similar problem but with various objectives. Additionally, powerful hybrid techniques were proposed to find the optimal solution of the same problem, such as the grid-based multi-objective harmony search (GrMHS) algorithm [27], and the quasi-oppositional swine influenza model based optimization with quarantine (QOSIMBO-Q) [28]. However, most of the aforementioned algorithms were only applied on small and medium-scale RDSs. Nevertheless, few works have been done so far considering the large-scale systems [29; 30; 31; 32]. The algorithms applied in those papers are: QO-teaching-learning based optimization (QOTLBO) [29], loss sensitivity factor-simulated annealing (LSFSA) [30], krill herd algorithm (KHA) [31], and stochastic fractal search algorithm (SFSA) [32]. Therefore, new and robust algorithms are needed to solve the OPDG problem, especially for large-scale RDSs with different combinations of the most important objectives regarding the optimal operation of radial systems. In this paper, a novel combined evolutionary algorithm, named the quasi-oppositional differential evolution Levy flights algorithm (QODELFA) is proposed. The quasi-oppositional based learning concept (QOBL) is applied to generate the initial population of the combination between the original differential evolution (DE) algorithm and the Levy flights (LF) perturbation. The new method is utilized for optimal sitting and sizing of DGs in RDSs by taking three objective functions (OFs) under consideration. Those OFs are the active power loss (APL) minimization, the voltage deviation (VD) improvement, and the voltage stability index (VSI) enhancement. Two types of DGs are studied based on their power factor (PF); i.e., unity and non-unity. Different combinations of the three OFs are considered while satisfying several operational constraints. The effectiveness of the proposed QODELFA is tested and verified on the IEEE 33-bus, 69-bus, and large-scale 118-bus systems, and the results are compared to many existing methods in the literature. The rest of this paper is arranged as follows: The detailed description of the proposed QODELFA is presented in Section 2. A performance comparison between QODELFA and several novel algorithms is also addressed in this section. The OPDG problem formulation is explained in Section 3.