## 2 Methodology of Literature Review
### Summary Analysis of Single Algorithms Extended to Multi-Objective Algorithms  
In [PERSON] et al. (2002) [8], Non-Dominated Sorting Genetic Algorithm II (NSGA-II) was presented by the authors. A novel methodology is suggested for effectively solving MOO difficulties, which addresses the drawbacks of earlier multi-objective genetic algorithms (GAs), like their high computational complexity and non-elitism approach. A more recent iteration of NSGA-II, known as NSGA-III [22; 23], was enhanced to handle numerous objective optimization problems with up to 15 goals inside by using a reference point [24]. In [PERSON] et al. [2], constrained decomposition with grids (CDG) is a technique that was proposed to address the problems of sensitivity to Pareto front shapes and diversity loss which could occur in decomposition-based multi-objective evolutionary algorithms. For example, a unique differential evolution approach for MOO was introduced and proven by [PERSON] et al. [18]. To replace the existing selection method, the research proposed a new one that makes it possible to apply differential evolution (DE) to the solution of both multi- and single-objective optimization problems. Comparing the trial population solution with its counterpart in the current population is a step in the selection process. The trial candidate will be chosen to live to the next generation and replace the present population vector if its dominance surpasses that of the current population member. If not, the current member of the population will be retained. They suggested rejecting the trial solution if it fails to meet any of the objectives of the target solution. Through its application to three multi-objective benchmark optimization problems, the strategy's validity was verified. The simulation results show that for the selected issues, the technique may produce an approximate Pareto front [14]. A variation of the generalized differential evolution (GDE) algorithm was presented by [PERSON] and [PERSON] in the year 2005, and it might be applied to both multi- and single-objective problems. The original differential evolution algorithm was applied to unrestricted single-objective problems. To produce more efficient distributed solutions in MOO problems, GDE3, an improved version of GDE, was applied [18]. In [PERSON], [PERSON] and [PERSON]. [PERSON] et al. (2005), to solve MOO problems, the authors used PSO [25]. This method involved using a separate set of particles to aid the other particles in their movements. [PERSON] [6] and colleagues (2006) presented the first proposal for a MOPSO, and offered a comprehensive analysis of several MOPSOs described in the specialized literature. We will categorize the approaches and list the salient features of each proposal as part of this study. The study's last part lists several topics in this field that we think may make good subjects for additional research. A hybrid method which combines the benefits of the PSO algorithm as well as genetic algorithms was proposed by [PERSON] et al. (2012). In addition, a neighbor search engine has been utilized as a local search operator for improving the quality of the solution. This engine concentrates on examining the less populated area of the external archive to increase the quantity of non-dominated solutions. To provide more solutions close to the personal best, [PERSON] et al. (2012) proposed incorporating a local search approach into the niching PSO. To particularly handle MOO problems, [PERSON], [PERSON] et al. (2012) created the bat algorithm (BA). An adaptation of the flower pollination optimization approach was created by [PERSON] et al. (2014) to solve MOO problems. [PERSON] et al. (2016) [3], for the first time, offered a cutting-edge method for effectively optimizing problems with multiple objectives, known as the multi-objective grey wolf optimizer (MOGWO). A fixed-sized external repository is combined into GWO. This helps in storing and getting back Pareto-optimal solutions.