## 1 Introduction
### Literature Review  
For reaching the targets of the ORPD problem, various optimization methodologies have been recommended such as deterministic algorithms and metaheuristic algorithms [4]. All of these algorithms comprise original deterministic algorithms, modified deterministic algorithms, original metaheuristic algorithms, modified metaheuristic algorithms, and crossbreed heuristic algorithms. Deterministic algorithms [5; 6; 7; 8] are the earliest proposed methods with the acceptable solution quality and the existence of many handicaps not satisfying readers. Quite the opposite, metaheuristic algorithms such as family line of GA [9; 10; 11; 12; 13; 14], family line of PSO [15; 16; 17; 18; 19; 20; 21; 22; 23], family line of DE [24; 25; 26; 27; 28], GSA groups [29; 30; 31], and other methods [32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52] are later proposed for good solution quality. Methods miming the rule of natural selection or heredities, relating to the terms of genetics, crossover, and mutation selection, are called GA. AGA [9] is a reform of the genetic algorithm. SARCGA has been recommended in [10] by using simulated binary crossover, binary tournament selection, and polynomial mutation distribution of RCGA. In [11], HLGA is an incorporation of loop method and GA in which striking global search characteristic of GA has been used to exploit large solution spaces, whilst the loop method has played a task of search within the predetermined zones found by GA. In [13], authors have solved the ORPD problem by applying standard GA with Roulette wheel-based selection mechanism and improved GA with classification selection mechanism. A new different version, called SGA [14], was introduced in 2018 for handling the ORPD problem considering power-loss minimization only. A different optimization technique, which is called particle swarm optimization, is also not less famous than GA. It includes original PSO [15] and lots of variants such as CLPSO [16], ALC-PSO [17], PSO [18], PSO-IPG [19], HPSO [20], HPSO-ICA [21], PSO-GT [22], and HPSO-TS [23]. HMAPSO is a combination of GA, multiagent system and the excellent characteristic of PSO. Therefore, the method is capable of finding solutions better than other implemented methods such as GA, PSO, HPSO, and MPSO. PSO-IPG [19] is more effective than PSO-AIW, PSO-AAC, SPSO-AAC, PSO-CF, PSO-PG, PSO-SWT, and PSO-PGSWT. A large DE group consisting of standard DE [24], DE-AS [25], MTLA-DDE [26], QODE [27], and IABC-DE [28] has been reapplied or newly recommended for the ORPD problem. The purpose of such methods is to overcome some shortcomings of DE, such as premature convergence to not very high-quality solutions and easily getting stuck into less effective solutions by improving main stages of DE or combination of DE and other methods with more potential characteristics. In DE-AS [25], authors have modified mutation and selection mechanisms of DE by applying VSMF and PST rule used in the ant system. Using both VSMF and PST has supported the method to achieve not only good solution quality but also avoid consuming time manner better than PSO and DE through IEEE 30-bus system with minimizing power-loss objective function. In addition to the mentioned method groups, a high number of other methods have been used for solving the ORPD problem via different systems with single objective as well as multiple objectives. These methods are GSA [29; 30; 31], ACO [32], QOTLBO [33], TLBO [33], GBTLBO [34], MGBTLBO [34], CSA [35], and ICSA [36], HAS [37], CKHA [38], HFA-NMS [39], GWO [40], ABC [41], EMOA [42], DSA [43], ALO [44], ICBO [45], GBB-WCA [46], JA [47], MFO [48], MOGWA [49], WOA [50], ABC-FF [51], and CSOA [52]. Among these methods, QOTLBO [33], GBTLBO [34], ICSA [36], HFA-NMS [39], ICBO [45], GBBWCA [46], and ABC-FF [51] are improved versions of original methods in aim to find more promising solutions than those of original methods for the ORPD problem.