## 2 Methodology of Literature Review
### Summary Analysis of Single Algorithms Extended to Multi-Objective Algorithms
This idea is manageable because it is likely to evaluate the ideal sites for every objective separately by utilizing Pareto fronts created from MOEAs. The real outcomes demonstrate that MOEAs could compete with SOEAs and manage many objectives simultaneously, particularly when examples are large or strongly correlated. In [PERSON] et al. (2020) [14], the authors used multi-agent systems and Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) to solve the multi-objective optimization problem (MOOP) with a focus on logistics management. The first solution for MOPs was stochastic fractal search (SFS) [36] designed by [PERSON]. [PERSON] et al. in 2020. The validation of SFS was limited to the CEC 2009 benchmark test. So, we do not know how effective it is on other benchmark problems like GLT and DTLZ. In [PERSON] et al. (2020), to overcome suspension optimization problems, a unique multi-objective quantum particle optimizer based on an archive was formulated (MOQPSO) [37]. The basic equations of QPSO were adjusted and more methods to handle various goals were included, thus forming the MOQPSO algorithm. In addition, [PERSON] et al. (2017) [7] presented an innovative multi-objective approach based on swarm intelligence to determine the optimal overtaking trajectory for autonomous ground vehicles. The purpose of this method was to enhance the time length of the gambit, visibility, and smoothness in the vehicle's path while considering mission-based limitations. In [PERSON] et al. (2021) [38], the authors recommended using a competitive method. A possible solution to MOO issues is given in the form of an integrated whale optimization algorithm (CMVOA). The study might be able to construct a stronger leader to control the update of whale groups by applying a new competitive procedure. This will be better for the algorithm's convergence and deliver better outcomes. Furthermore, it is worth mentioning that the competitive procedure uses a more advanced crowding distance computation where multiplication replaces the standard addition operation. This adjustment helps to represent population density more precisely. Differential evolution (DE) is a different method used to make the population diverse. We can use many changing techniques for optimizing important DE parameters, which improves overall efficiency. A thorough assessment is conducted of the suggested comprehensive multi-objective optimization algorithm (CMVOA) on various benchmark functions that show distinct versions of the true Pareto front. Based on the outcomes of various performance metrics, it is shown that in most situations, the suggested CMVOA method performs better than the other three methods. The discussion also includes implications of model parameters. Three real-world instances showed an effective application of the proposed CMVOA algorithm, which offers more evidence for its usability. In [PERSON] et al. (2021) [39], to lessen the drawbacks of addressing MOO problems, an improved and extended version of the whale optimization algorithm (WOA) is suggested. Their improvements included (i) modification of the standard WOA's distance control factor to include dynamically generated values rather than a fixed value; (ii) balancing the movement towards the opposite of the optimal solution and its original values depending upon a specific probability to prevent being stuck in local minima; and (iii) speeding up coverage and convergence using the Nelder-Mead method and the Pareto archived evolution strategy. By contrasting the suggested method with nine reliable multi-objective algorithms, its efficacy is assessed. Three benchmark multi-objective test functions (which are CEC2009, DTLZ, and GLT), totaling twenty-five test functions, are used in this evaluation. The experiments demonstrate how the suggested algorithm performs better than some of the existing multi-objective algorithms that have been published in the literature. In [PERSON] et al. (2021) [40], the authors offer a thorough review of the existing body of research on MOO that has already been conducted.