## 4 Analysis of Single Objective Algorithms Overview
### Practical Swarm Optimization
Fitness Dependent Optimizer Is a Technique Used to Solve Problems with a Single Aim in Optimization

The FDO with single-objective scout optimization problems (FDOSOOP) process begins with the placement of fake scouts at random positions on search landscape inside the designated higher and lower borders. The optimal solution discovered thus far isselected as the global best solution at the end of every iteration. Next, Equation (2) is used for calculating the fitness value \(fw\) for each one of the artificial scout bees. The value of \(fw\) is then checked to see if it equals 1 or 0, and if it does, \(xi\), \(fitness\) also equals 0. The pace is produced using Equation (3). A random number \(r\) will be generated between \(-1\) and 1 if \(fw\) is less than 1 and more than 0. The speed is calculated using Equation (4) if the value of \(r\) is smaller than zero. \(fw\) is given a negative sign in this case. Equation (5) is used for calculating pace, however, if \(r\) is larger than or equal to zero. As a result, \(Zw\) is given a positive sign. A random assignment of a positive or negative sign to a \(fw\) will guarantee that the artificial bee searches in a random manner in every direction. In FDO, the randomization method influences both the direction and size of pace; in such cases, the pace size is dependent on the \(fw\). In most other situations, the randomization procedure just impacts the pace direction. Moreover, the artificial scout bee uses the fitness function that has been shown in the pseudocode of single-objective FDO (refer to Algorithm 4) to determine whether the newly discovered solution is superior to the current one. The previous solution is ignored and the new answer is accepted if it is better. Moreover, a noteworthy feature of FDO is that the artificial scout bee will continue to pursue the previous path (using the previous _pace_ value if it exists) in the case when the new solution does not produce better results--that is, if it is beneficial. The scout bee is searching for a better solution. Moreover, the FDO method will stick with the current solution until the next iteration if using the previous position is not helping the scout bee discover a better one. With this method, the _Pace_ value is saved after each acceptable answer in case it is needed in a later iteration. The readers that are interested in learning more about single-objective FDO can refer to [20]. ```
1: Initialize scout bee population randomly Xi (i = 1, 2, 3,.... N). 2: While (t) iteration limit not reached (m)
3: or solution good enough. 4: For every one of the artificial scout bee Xt,i
5: Finding the optimal artificial scout bee Xt,best
6: Generating random walk r in [-1, 1] range
7: If (Xt,i fitness = 0)(for avoiding division by 0)
8: Fitness weight = 0
9: Otherwise
10: fitness weight value is calculated with the use of Equation (6)
11: Endlf
12: If (fitness weight = 1 or the fitness weight = 0)
13: Pace calculation with the use of Equation (7)
14: Otherwise
15: If (random number \(\geq\) 0)
16: Pace calculation with the use of Equation (7)
17: Pace calculation with the use of Equation (9)
18: Otherwise
19: Pace calculation with the use of Equation (8)
20: Endlf
21: Endlf
22: Calculating Xt + 1, i using Equation (5)
23: If (Xt + 1,i fitness < Xt,i fitness)
24: Accepted and saved pace is moved
25: Otherwise
26: Calculate Xt + 1, i with the use of Equation (5) with the preceding pace
27: If (Xt + 1,i fitness < Xt,i fitness)
28: Accepted and saved pace is moved
29: Otherwise
30: The current position is maintained// (no move)