## 1 Introduction  
The power distribution systems generally operate radially with meshed topologies thanks to normally open switches and normally closed switches consisting in the systems [1]. In addition, the electrical power distribution network reconfiguration problem is recently becoming more interesting towards active distribution networks with and without distributed generations. Many research papers in the literature review have investigated to search for the switches which are optimal tie-switches and sectionalizing switches in the distribution systems. Regarding network reconfiguration issues, many strategies and algorithms are developed to get power loss reduction, increase power quality, and maintain radial distribution system topology. The modern network technique for reconfiguration is provided in [2]; this paper investigated the problem formation included in multi-objective function, which considered in minimizing the number of switches operation, voltage constraints, and loading limits of feeder and transformer. The fundamental loop concept has been introducedinto the reconfiguration of distribution system problem and tested on many evolutionary algorithms (EA), which proved to be good integration solving power distribution system problem with fast convergence [3]. In [4], the authors proposed a new technique called the sequential switch opening and exchange (SSOE) method, which provides an accurate result and converges to optimum solution by considering to open any sectionalizing switch with the minimum value of power loss; this proposed method can solve the issue of large-scale systems and obtain an accuracy of the result, unlike other EA, which has a different optimum solution with a different running time. The power losses reduction through the distribution system reconfiguration have been provided using different algorithms such as [PERSON]'s algorithm [5], Ant Colony [6], Extended invasive weed optimization [7], Cuckoo search [8], Opposition learning genetic algorithm [9], Genetic algorithm [10], Grasshopper optimization [11], Augmented grey wolf optimization [12], Vector shift operation [13], Salp swarm optimization [14], Particle swarm optimization [15], Improved fireworks [16], and Backpropagation neural network [17]. Furthermore, the authors in [18] had implemented the selective firefly algorithm and search space reduction for the complex problem of distribution system reconfiguration; a set of switches for opening based on mesh analysis is initially provided and eliminated from the switches possibility as search space based on lowest active power of branch. The distribution system reconfiguration using qualified binary particle swarm optimization is authorized by [19]; two different power flows have been compared to perform the optimal reconfiguration. An optimal reconfiguration with multi-objective of reduced active power and reactive power losses as well as reliability improvement are studied in [20]; the authors had also applied particle swarm optimization to search for that optimal reconfiguration. This multi-objective is also investigated in [21] using an evolutionary algorithm. Moreover, to tackle with huge search space for reconfiguration, recent researchers have implemented novel techniques for reconfiguration. Two-stage optimizations as a fast optimal system reconfiguration have been provided in [22]; the authors have proposed a method to simplify the system into a simplified system graph, and this is applied for initializations and generations guiding of the algorithm, and it is implemented with firefly algorithm. A sequential switch opening method is implemented in [23] to aim at searching for feeder reconfiguration; this paper is based on minimum active power flow due to replacing impedances by resistances in the branches. The sequential opening of branches has been developed in [24] to search for optimal distribution topology in the real system; the authors had compared the simulation and experiment based on the active distribution system testbed for research and teaching.