## 4 Analysis of Single Objective Algorithms Overview
### Learner Performance-Based Behavior
Alearner's general study behaviors will be affected when their metacognitive capacity is randomly impacted [56; 57]. As per [57], the learner's level of metacognition is impacted by the many methods they are trained in. This research does not involve the implementation of such solutions. As a result, the system could determine the rate at which learners' metacognition changes. As mentioned before, learners' behaviors may be randomly influenced by their level of metacognition. This could be achieved by updating the values of the learner's studying behaviours at random or by randomly changing the individual's behavior positions at a specific rate. The algorithm uses the genetic algorithm's mutation operator; for further information on the algorithm, see [21]. The LPB algorithm's pseudocode is shown in Algorithm 5. Symbol definition:  
M represents the initial random population  
N represents the number of individuals in the new population  
dp represents the percentage of the individuals that have been selected from M  
O represents a subpopulation that has been selected from M based on dp operator. GP represents the good population  
BP represents the bad population  
k represents a counter that is utilized for counting the number of individuals that have been newly created  
PF represents the perfect population  
```
1: [Initialization] A population M is randomly created
2: [parameters are specified]
3: The number of the required learners N for some departments, mutation rate and crossover rate are specified. 4: [Subpopulations are Created]
5: The dp parameter is utilized for the random selection of a percentage of the individuals O from M. The fitness of the individuals in O is evaluated based on their level of fitness, individuals are sorted in O (i.e., in a descending manner), one of the sorting methods is used, and O is divided into 2 good populations (individuals that have a high level of fitness) and bad (individuals that have a low level of fitness). 6: While the condition of termination isn't met
7: The dp parameter is utilized for randomly choosing a percentage of the individuals O from M
8: Individuals' fitness is assessed in O. Depending upon their level of fitness, individuals are sorted in O (in a descending manner), and one of the sorting approaches is used. 9: The group of O is divided into 2 populations, which are good (individuals that have a high level of fitness) and bad (individuals that have a low level of fitness)
10: Fitness is found for all of the individuals in M
11: Highest fitness is found in the bad as well as good populations
12: In the case where an individual from M has fitness \(\leq\) highest fitness in bad population
13: Then it is moved to BP
14: Elself
15: an individual from M has fitness \(\leq\)highest fitness in GP
16: then it is moved to GP
17: Else
18: It is moved PF
19: Endlf
20: while k \(\leq\) N
21: in the case where PF isn't empty an individual is selected from PF

### A Comprehensive Analysis of the Benefits, Drawbacks, and Practical Uses of Single Algorithms

Within the optimization field, individual algorithms are essential tools with unique strengths and weaknesses. This section comprehensively analyzes the EO, DE, PSO, FDO, and LBP single algorithms, highlighting their advantages, disadvantages, and practical uses in various real-world optimization issues. Table 2 presents a thorough examination of individual algorithms, offering a full analysis of their benefits, limitations, and practical applications in different optimization scenarios. This table provides readers with significant insights into the effectiveness and relevance of different algorithms in solving complicated real-world problems by carefully examining their unique characteristics.