## 4 Analysis of Single Objective Algorithms Overview
### Learner Performance-Based Behavior
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Single** & **Advantage** & **Disadvantage** & **Applications** \\ \hline PSO & - & Has high effectiveness in many & Prone to falling into the local & - & Used for solving a very large \\  & & different applications, which have & optimum & number of real-world \\  & & the capability of producing & & optimization problems \\  & & excellent results & & [54; 58; 59]. \\  & - & Relatively simple to implement & & \\  & - & Very low computational & & \\  & - & Low cost & & \\ \hline FDO & - & Using a fitness function for the & - & Consuming more time and & - & Aperiodic antenna array \\  & & generation of proper weights that & space & & designs \\  & & are helpful for the algorithm in & - & The performance of the & - & Frequency-modulated sound \\  & & the exploration phase & & algorithm depending on the & waves \\  & - & It has important feature in which & number of search agents & - & GAP, which stands for \\  & - & the past search agent pace (i.e., & & Generalized Assignment \\  & velocity) is stored for possible & & & Problem, is a well-known \\  & & re-use in future & & & combinatorial optimization \\  & - & Similar to PSO mechanism in & & problem that is classified as \\  & - & updating agents’ position & & NP-hard [20]. \\  & - & Fast convergence to global & & \\  & - & Optimal & & \\  & - & Proven effectiveness to solve & & \\  & & real-world problem & & \\ \hline LPB & - & Provides good balance between & - & Sizes for combinatorial & GAP [21]. \\  & & the phases of exploitation and & optimization & \\  & exploration & & & \\  & - & Convergence speed & & \\  & - & The ability to avoiding local & & \\  & & optima batter than other & & \\  & - & Algorithms such as PSO & & \\  & - & Using mutation and crossover to & & \\  & & make good change in the & & \\  & structure of individual & & \\  & - & Proven effectiveness to solve & & \\  & real-world problem & & \\  & - & Improving the randomness and & & \\  & - & time processing & & \\  & - & A percentage of population is & \\  & - & separated. & & \\  & - & Population is divided into & \\  & multiple subpopulations. & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._(2022) [67]. The prevailing approach to such problems typically focuses on combining many goals into a single objective and takes group dynamics into account [41]. ### Multi-Objective Basic

It was [PERSON] who invented MOO. A vector that represents the objective function is contained in MOO. The vectors of the goal function rely on the vector of the solution. There are various options that could be taken into consideration within the context of MOO, rather than a single, universally applicable best solution [68]. The MOO problem's mathematical representation is expressed in Equation (10) ([PERSON], 2001) [8]. \[Max/\mathit{Minf}_{1}(x),f_{2}(x),f_{3}(x),f_{4}(x)\ldots\ldots \ldots f_{n}(x) \tag{10}\] \[\mathit{Subject\ to:}\ X\in U\]

Assuming U is the feasible set, \(f_{n}(x)\) is the n to nth objective function, x represents the solution, n represents the number of the objective functions, and min/max represent the total number of object operations [8]. The objective function vector as well as the choice variable space of the solution vector are both contained in the multidimensional space that makes up the MOO. There is a matching point in the space of the objective function for every solution x in the space of choice variables ([PERSON], 2001) [1,8]. Since we are only concerned with maximizing one function, finding the best solution in the setting of a single-objective problem is simple. If and only if solution A achieves a smaller objective value than solution B in a minimization problem, then solution B is preferable to solution A. In MOP, on the other hand, we seek to find a solution that concurrently maximizes each of the objectives. The relational operators cannot be used to compare the solutions.