## 4 Analysis of Single Objective Algorithms Overview
### Multi-Objective Applications  
MOO, which stands for multi-objective optimization, is a strong method used in various areas to handle complex decision-making problems with clashing objectives. The use of MOO techniques has been rising in popularity because they can effectively find the best solutions that balance many different criteria at once. In the table, it is clear that MOO applications are spread across many areas like engineering design, systems for renewable energy, healthcare, and finance. Every field of application has its own problems and objectives which need specific optimization techniques and algorithms to be applied. Complex systems and processes can be optimized by researchers or practitioners through the use of MOO approaches. These methods consider multiple contradictory standards in \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Multi-Objective Algorithms** & **Advantage** & **Disadvantage** & **Methods** & & **Applications** \\ \hline \multirow{8}{*}{MODE} & - & Fast convergence & & & - & Four-bar truss design problem, \\  & - & More effective & & & & - & Two constrained problems (disk brake design problem [81, 82]) \\  & & computational result & - & The issue is determining the value of fitness sharing radius & - & Parato approach & - & The 4-bar truss design problem [81, 82] \\  & & compared to other algorithms & - & - & Parato approach & - & The 4-bar truss design problem [81, 82] \\  & - & More accuracy to represent the real Pareto front & & & & - & - \\ \hline \multirow{8}{*}{MOPSO} & - & Very useful for real-world applications & & & & - & Four-bar truss design problem, \\  & - & Uses maximum fitness function to determine & & & & - & Two constrained problems (disk brake design problem [81, 82]) \\  & - & Provides good diversity & Easy to fall into local optimum in the high-dimensional space approach & Parato (non-dominated) approach & - & Multi-objective 0/1-knapsack problem. \\  & - & Leader is stored in an external archive selected randomly & - & Low rate of convergence & - & - & Pseudo-Boolean discrete problems problems \\  & - & Different leaders choosing each particle for each decision variable to conform to a single global best & & & & - & Multi-objective 0/1-knapsack problem. \\ \hline \multirow{8}{*}{MOPSO} & - & Uses fitness weight and weight factor parameter to increasing the convergence and algorithm & - & Complex computation & Parato (non-dominated) approach & \\  & - & Provides good distribution solutions, enabling the decision-makers to have more variant options to take under consideration. & - & The performance of the problem the problem the algorithm on the natural order of the problem like other algorithm & - & External Archive [48] \\  & - & Time and space will decide on the decision-makers to have more variant options to take under consideration. & - & Hypercube grids & \\  & - & Time and space will increase linearly & & & & \\ \hline \multirow{8}{*}{MOLPB} & - & Provides better competition & & & & - & The 4-bar truss design issue \\  & - & Good diversity computation & - & Complex computation & & - & The coil compression spring design problem \\  & - & The performance of the problem & - & - & approach and crowding distance & - & The pressure vessel design problem [47] \\ \cline{1-1}  & - & Proper method for providing & & depending on the natural order of the problem like other algorithms & - & External Archive & - & The car side-impact design problem \\ \cline{1-1}  & - & Pareto-optimal solution for a variety of multi-objective problems & & & & - & The speed reducer design problem \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._order to efficiently achieve the desired outputs. The references provided in this table are significant and influential works, as well as contemporary contributions to the research, within each particular field of application.