### Contributions and Scope  
To find a strategy that allows reducing the computational resources and time required to carry out an optimization process, the idea is to communicate two specialized software applications in order to achieve an optimal global solution. As shown above, several optimization methods have been implemented to solve the D-STATCOMs sizing and placement problem. However, most of these solutions can get stuck in local optima, which is why this study implements an optimization methodology that connects the GAMS and MATLAB software interfaces through a master-slave methodology. The benefits of this methodology include the fact that the D-STATCOM's placement and sizing problems can be staged. The master stage, which uses the DSCA to solve the D-STATCOMs placement problem, is programmed in MATLAB software. Then, using the BONMIN solver in the GAMS software and the locations provided by the master stage, the mathematical model that represents the problem are solved, finding the optimal size of the D-STATCOMs and the value of the objective function that meets all the technical-operating conditions of the system. Listed below are the main contributions of this research article: \begin{table}
\begin{tabular}{l l l} \hline \hline
**Method/Algorithm** & **Objective Function** & **Year** & **Ref.** \\ \hline Genetic algorithm & Minimization of power losses & 2011 & [26] \\ Artificial neural networks & Mitigation of voltage sags under faults & 2012 & [27] \\ Immune algorithm & Minimization of power losses and reduction of investment and operating costs & 2014 & [28] \\ Particle swarm optimization & Minimization of power losses and voltage profile improvement & 2014 & [29] \\ Ant colony optimization & Minimization of power losses and voltage profile improvement & 2015 & [18] \\ Sensitivity indices & Minimization of power losses and voltage profile improvement & 2015 & [30] \\ Harmony search algorithm & Minimization of power losses & 2015 & [31] \\ Heuristic search algorithm & Minimization of power losses & 2016 & [32] \\ Imperialist competitive algorithm & Minimization of energy costs and voltage profile improvement & 2017 & [33] \\ Discrete-continuous vortex search algorithm & Investment and operating costs reduction & 2017 & [34] \\ Modified crow search algorithm & Reducing line losses, maximizing economic benefits, improving & 2018 & [35] \\ Particle swarm optimization & Reducing of power losses and voltage profile improvement & 2019 & [20] \\ Hybrid analytical-coyote & Minimization of active power losses and voltage profile & 2019 & [36] \\ Modified sine-cosine algorithm & Minimization of power losses and voltage profile improvement & 2020 & [37] \\ Discrete-continuous vortex search algorithm & Reduction in investment and operating costs & 2021 & [2] \\ GAMS software for the solution of the exact & Reduction in investment and operating costs & 2021 & [38] \\ MINLP model & Mixed-integer second-order conic & Minimization of power losses and reduction of investment and operating costs & 2022 & [25] \\ Programming & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overview of methodologies used in the literature for sizing and locating D-STATCOMs. A complete description of the mathematical formulation representing the problem under study while considering different load types in a daily operation scenario. 2. The implementation of a new optimization methodology that uses MATLAB and GAMS software interfaces in order to find the optimal global solution to the problem of sizing and locating D-STATCOMs in electrical distribution networks with a meshed or radial topology. 3. A new form of the master-slave method to solve the mathematical model representing the studied problem. In the master stage, MATLAB software is used as a tool to develop the discrete version of the sine-cosine algorithm, with the aim of determining the locations of the D-STATCOMs.