## 2 Methodology of Literature Review
### Summary Analysis of Single Algorithms Extended to Multi-Objective Algorithms
The effectiveness of this method is determined by its use in real-world engineering design problems as well as mathematical ones. The outcomes demonstrate that employing the suggested methods could create significant enhancements in handling the complex problems under investigation. In [PERSON] et al. (2022) [44], the authors with complicated optimization problems; for instance, real-life engineering design optimization issues were handled by the researchers introducing a fresh multi-objective equilibrium optimizer (MOEO). One metaheuristic algorithm that was recently published and finds its base in physics is known as the equilibrium optimizer (EO). It takes inspiration from models which forecast dynamic and equilibrium states. The MOEO technique uses a similar process, including models in a new target search area. During the evolution of the search, crowding distance was applied to balance between exploring and exploiting stages. The MOEO algorithm keeps an important aspect in multi-objective metaheuristic algorithms, which is population variation through a non-dominated sorting technique. Optimal solutions are used for maintaining and improving Pareto's extent with the use of a repository with an update function. The evaluation of 33 contextual problems, comprising 12 unconstrained, 6 constrained, and 15 realistic constrained engineering design challenges, such as non-linear problems, confirmed the efficacy of MOEO. The performance of the suggested MOEO algorithm was compared to that of existing state-of-the-art MOO methods. The results show that the recommended MOEO produces more competitive outputs than the other algorithms, both in terms of number and quality. The findings obtained for all 33 benchmark optimization problems provide a comprehensive description and clarification of the robustness, efficiency, and exploratory potential of the MOEO algorithm for solving multi-objective problems. In [PERSON] et al. (2022) [45], the five ADMET properties were taken into account, MOO was used, a model was created to map the relation between molecular descriptors as well as their biological activities, and the process of choosing molecular descriptors was studied. To choose possible medication candidates for the treatment of breast cancer, a thorough process was created. More specifically, the next contributions are presented in this study. Different perspectives on unsupervised spectral clustering are used to construct a new feature selection technique. The selected features demonstrate decreased redundancy and an improved ability to represent comprehensive information. The issue is resolved by enhancing and applying the AGE-MOEA algorithm14, which is founded on the examination of the interaction between conflicting optimization targets. With regard to search performance, the improved approach performs better than many existing algorithms. The process of choosing candidate compounds with anti-breast cancer characteristics has been aided by the establishment of a thorough framework. [PERSON] et al. (2022) [46] suggest the MOGWO, which is a novel method that can be used to efficiently organize material transport systems with a single, intelligent mobile robot. The optimization process that is suggested entails a careful analysis and the mathematical development of thirteen new fitness functions. The MOO problem is solved by coupling such functions to form a Pareto front. Furthermore, a novel approach to efficiently explore the search space of numerous objectives is shown. In addition, three advanced meta-heuristic approaches (MOAOA, MOGA, and MOPSO) and the suggested enhanced MOGWO algorithm are quantitatively evaluated and compared for their efficiency on 25 benchmark problems using four metrics: inverted generational distance (IGD), maximum spread (MS), generational distance (GD), and spacing (SP). Results from two experimental settings showed that the improved MOGWO is superior in terms of the coverage, convergence, and robust solution generation when compared to previous algorithms.