## 2 Methodology of Literature Review
### Summary Analysis of Single Algorithms Extended to Multi-Objective Algorithms
The process of gathering this data is then utilized to establish a social ranking and simulate hunting behavior among grey wolveswithin multi-objective search situations. The new approach's performance was evaluated on ten multi-objective benchmark issues, and compared to two popular meta-heuristics: MOPSO and the multi-objective evolutionary algorithm based on decomposition (MOEA/D). In [PERSON] et al. (2017) [4], to tackle the difficulties caused by MOO problems, a multi-objective variant of the whale optimization algorithm (MOWOA) was created by the authors. In [PERSON] and [PERSON]. [PERSON] et al. (2017) [26], to deal with the difficulties brought about by multi-objective particle swarm optimization problems (MOP), the writers made a new version of PSO. Inside this edition, they introduce a new global margin ranking (GMR) that uses positional information from individuals in the objective space to define dominant borders across a population. In [PERSON] et al. (2017) [7], the aero-assisted spacecraft trajectory optimization problem was dealt with by the authors through presenting a new multi-objective technique. This algorithm was created using an enhanced version of the NSGA-II algorithm, including a discretization method for formulation and parameterization. In [PERSON] et al. (2017) [27], a new version of the ant lion optimizer (MOALO), which is based on how ant lions hunt and interact with ants, was presented by the writers. At first, the repository keeps all non-dominated Pareto-optimal solutions that have been discovered until now. In this case, we use a roulette wheel mechanism to select the best responses from this set. The roulette wheel considers the number of antlions linked to every solution for finding out which multi-objective search regions are most excellent. Several standard unconstrained and limited test functions are used to show the effectiveness of the method being studied. This methodology is applied to tackling various multi-objective engineering design problems. In [PERSON] et al. (2018) [10], the authors proposed tackling the multi-objective multilevel thresholding image segmentation task using the weighted optimization algorithm (WOA). Furthermore, there is the GWO. In [PERSON] et al. (2018) [28], to address MOO problems, the authors proposed integrating the grasshopper algorithm with the archive and goal selection approach. In [PERSON] et al. (2019) [29], to handle multi-objective engineering design problems, the authors presented a multi-objective sine-cosine algorithm (MOSCA). To produce a range of non-dominated solutions and maintain the diversity among them, it makes use of elitist non-dominated sorting and crowding distance techniques. In [PERSON] et al. (2019) [30], to solve MOO problems, the authors introduced the artificial sheep method, which combines an archive as well as leader selection method with Pareto-based theory. In [PERSON] et al. (2019), the authors proposed a multi-modal multi-objective differential evolution (MMOEA) optimization algorithm [31] as a solution for Pareto multi-modality problems, where the Pareto set is composed of numerous distinct subsets. By allowing offspring outside the search space to experience additional mutations, MMOEA utilized a new mutation-constrained approach to reduce the concentration regarding individuals located at the search space boundaries. A similar analysis has been carried out by [PERSON] and [PERSON] (2017) [32] to evaluate Pareto-optimum solutions on the Pareto-optimal fronts of different geometries. In [PERSON] et al. (2019) [33], for this objective, the authors used an enhanced version of the inverted generational distance indicator. Fuzzy logic is used in certain research in the literature to improve evolutionary MOO (EMO). Via [PERSON] et al. (2019) [34], several methods have been proposed to deal with such problems, which include using weighting methods for reducing multi-objective problems to only one objective.