## 2 Methodology of Literature Review
### Summary Analysis of Single Algorithms Extended to Multi-Objective Algorithms
This covers the basics of optimization theory, commonly used MOO algorithms, and the field's recent developments in nuclear science and engineering. The techniques presented in this study are intended to investigate the user's design space to identify a set of best practices. Typically, such solutions are evenly distributed across the Pareto front and non-dominated. This gives the designer a variety of possibilities to pick from, none of which would improve one objective function without sacrificing another. Two different categories of MOO methods are presented: meta-heuristic and classical. Those groups are then divided into four different approaches (progressive, a priori, no preference, and a posteriori) which are used to determine which answers are the best. Every approach discussed has benefits and drawbacks that vary depending on variables including user participation volume, solution sets' efficacy, and implementation costs. This study gives academics a thorough understanding of the MOO algorithms that are presently available. Additionally, it provides a basic understanding of how these methods can be used in a broad range of problems in the disciplines of engineering and nuclear science. A thorough understanding of how to apply these techniques to a variety of problems in the disciplines of engineering and nuclear science. In [PERSON] et al. (2021) [41], the authors described a computational technique that generates solutions for multi-objective problems inside the Pareto subset using the LibreOffice Calc NLP Solver. This work examines the performance of a solver intended for single-objective problems on multi-objective problems. Several solutions near the Pareto front are produced by giving the objectives random weights. Since the solver is inherently random, the suggested solutions are positioned close to the front rather than directly there. This is not an acceptable scenario from a mathematical standpoint. Practically speaking, though, it is better to have even acceptable answers when no alternatives are available. In [PERSON] et al. (2021) [42], the authors provide a thorough examination of the popular MOO method NSGA-II for combinatorial optimization tasks. The assignment problem, vehicle routing problem, traveling salesman problem, allocation problem, scheduling problem, and knapsack problem are among the problems that NSGA-II has been utilized to solve. Three NSGA-II versions can be applied: Modified NSGA-II, Conventional NSGA-II, and Hybrid NSGA-II. In addition to analyzing the modifications made to NSGA-II, the researchers look at the many approaches taken to evaluate its effectiveness, including statistical testing, case studies, performance metrics, test instances, and benchmarking against other cutting-edge algorithms. This work also provides a succinct bibliometric overview of the research that was conducted. In [PERSON] et al. (2021), to deal with the drawbacks of DEED situations, the authors suggested a method using proportional dynamic adjustment decision (PDAD) variables considering the unit's power-generating range. The dynamic slack variable (DSL) approach can be described as a new way that brings in better constraint management methods from the slack variable approach. Additionally, they introduce NSDESa_LS which is an original variation of the sorting differential evolution method. To effectively tackle the DEED challenges, the solution uses a local search operator along with a self-adaptive parameter operator. Lastly, how well the suggested approach works is compared against other current techniques on systems of 5, 10, and 40 units. The outcomes demonstrate the good performance of this NSDESa_LS-PDAD technique. In [PERSON] et al. (2021) [43], motivated by the concept of constructing crystal structures, a multi-objective crystal structure algorithm (MOCryStAI) was introduced recently. The main three parts in the approach are the grid, choosing leaders, and archiving.