_Applied_  
_sciences_  
_Article_  
**Improved Hybridization of Evolutionary Algorithms**  
**with a Sensitivity-Based Decision-Making Technique**  
**for the Optimal Planning of Shunt Capacitors in**  
**Radial Distribution Systems**  
**[PERSON] 1,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,19,12,19,20,21,22,23,24,25,26,27,28,29,29,2,20,21,22,24,29,26,27,28  
problems effectively and efficiently. Those inevitable problems, such as high voltage drops, low voltage stability, bad quality and reliability of power delivered to the end consumers, and, as the main challenge, high power losses, have serious economical and functional impacts on the overall power system [1; 2]. The presence of all those crucial problems in the RDS, which lacks any controllability due to the absence of active or reactive power generation, leads to critical operations of RDSs under high stress and near their permissible limits. As a result of that, many severe blackouts have happened in the last decades around the world. Most of the blackouts started because of voltage collapse issues in RDSs [3; 4; 5]. It is essential for the RDS's operators to keep the system's operation under safe and reliable conditions by overcoming the aforementioned problems. Sufficient planning of reactive power compensation (RPC) in RDSs by optimally installing and sizing shunt capacitor (SC) units at some of the system's buses is one of the best ways that has been recently used to achieve the desirable optimal operation of RDSs. The improvement of the system's voltage profile and stability, the enhancement of the delivered power quality and reliability, the decrement of total losses and costs are some of the benefits that could be achieved by the RPC in RDSs. The optimal planning of shunt capacitors (OPSC) includes deciding the most suitable buses as optimal locations for the installation of SCs along with their optimal sizes. The OPSC should be done so that a defined objective function (OF) is optimized (e.g., energy loss or total cost minimization) which is usually non-differentiable, while satisfying several constraints (such as voltage limits and power balance). Hence, the OPSC is regarded as a non-linear mixed-integer optimization problem, which is relatively hard to solve by the conventional optimization methods, especially with the exponential increasement of its size along with the increased RDS's size [6; 7]. Recently, several evolutionary algorithms (EA) have been applied on the OPSC problem. EA methods have shown their ability to find the global optimal solution. Some of the latest applied algorithms include cuckoo search algorithm (CSA) [8], bacterial foraging optimization algorithm (BFOA) [9], modified monkey search (MMS) algorithm [10], flower pollination algorithm (FPA) [11], and ant colony search (ACS) [12]. In [13], a comparison between five original EAs has been presented. With the diversified EA techniques that have been utilized to tackle the OPSC problem, different OFs have been studied, such as power loss minimization [2; 9; 14; 15; 16]. In [17], both power loss minimization and voltage profile improvement were included in the studied OF, whereas a combination between power loss reduction and voltage stability enhancement was constructed as the OF in [18]. However, as noticed from several papers in the literature, the total cost minimization was the most addressed OF regarding the OPSC problem in RDSs. Various formulas of this OF were studied utilizing many EA methods, as in [19], where minimizing the SC costs was a main part of the formulated bi-objective. The cost of both total installed capacitors and power loss was the considered OF in [20], where a fuzzy-based technique was used for solving the OPSC problem. Similar cost minimization OF was employed in some other studies but with a variety of implemented EAs, as for instance: novel improved differential evolution (HCODEQ) [21], whale optimization algorithm (WOA) [22], gravitational search algorithm (GSA) [23], ant colony optimization (ACO) [24], and FPA [25].