## 1 Introduction
Among these methodologies, the following stand out: analytical methods, neural networks-based methods, combinatorial optimization algorithms, sensitivity algorithms, and the combination between metaheuristic methods and sensitivity indicators. The authors of [10] present the combination of a Chu and Beasley genetic algorithm (CBGA) and a specialized power flow algorithm for distribution networks, with the purpose of siting and sizing D-STATCOMs in distribution networks. The main contribution of this methodology is the possibility of working with radial and meshed distribution networks. The numerical results of the proposed CBGA in the IEEE 33- and 69-node systems showed greater reductions in the network's annual operating costs in comparison with the solution of the exact model in the GAMS software. Other methodologies presented in the specialized literature for the optimal integration and sizing of D-STATCOMs in distribution systems include the particle swarm optimization algorithm with a discrete-continuous coding [11], bat-based optimization [12], methods based on exact mathematical programming from quadratic next-integer programming models [13], and mixed-integer second-order cone programming [5]. Table 1 summarizes the main contributions made by the literature regarding the problem of the optimal placement and sizing of D-STATCOMs in distribution networks. The main characteristics of the optimization approaches in Table 1 are as follows: (i) the most common objective function is power losses minimization; (ii) there are two tendencies regarding the solution of the D-STATCOM optimal location and sizing problem: the first involves metaheuristic optimization techniques, and the second one proposes convex formulations or combines convex and combinatorial methods; and (iii) during the last decade, the problem regarding reactive power compensation in electrical systems continues to be a research area of interest for academia and the industry. The main contributions of this research are listed below:  
1. The optimal integration of D-STATCOMs in distribution systems by applying the SSA through a discrete-continuous coding. 2. The combination of the SSA and the specialized power flow algorithm for distribution networks known as the generalized backward/forward method, which allows working with radial and meshed topologies with no modifications to its iterative formula. It is important to mention that the combination between the SSA and generalized iterative sweeping constitutes a new master-slave optimization strategy that is applicable to the placement of parallel compensation devices in radial and meshed distribution systems, which has not been previously proposed in the specialized literature. Note that the selection of the salp swarm algorithm (SSA) to determine the best nodal locations for the D-STATCOMs in distribution networks was based on the following facts: (i) the SSA is a recently developed optimization methodology with excellent numerical performance in both nonlinear and mixed-integer optimization problems [18], which has not previously been applied to locate and size reactive power compensators in distribution networks; and (ii) the computational implementation of the SSA is simple and only requires some abilities in mathematical programming. In addition, it has a few evolution rules that simplify its numerical validation in any programming language. The rest of this work is structured as follows. Section 2 presents the mathematical formulation of the problem regarding the optimal integration of D-STATCOMs in distribution systems by means of a mixed-integer nonlinear programming model. Section 3 describes the solution methodology based on a master-slave optimization algorithm. In the master stage, the SSA is implemented, which defines the nodes for placing the D-STATCOMs, as well as their optimal size, by means of a discrete-continuous coding. In the slave stage, a technical evaluation of the solution is carried out by solving a multi-period power flow via the generalized backward/forward method.