Table 2 The parameter settings of the suggested optimization algorithm.
Parameter | Value |
|---|---|
Population size | 50 |
Number of iterations | 200 |
Initialization method | Logistic chaos mapping: \(\:{x}_{t+1}=r{x}_{t}(1-{x}_{t})\), where \(\:r\) is between 3.57 and 4 |
Improved diversity: utilizes a sine map for initialization purpose, \(\:{x}_{t+1}=\text{s}\text{i}\text{n}\left(\pi\:{x}_{t}\right)\), which improves diversity in comparison with standard Logistic Chaos Mapping. | |
Exploration formula | \(\:{X}_{t+1}={X}_{t}+Levy\times\:{C}_{jump}\times\:rand\), where |
Levy distribution: \(\:Levy\:\left(\beta\:=1.5\right)\), that models exploration motions | |
Jump coefficient \(\:\left({C}_{jump}\right)\): it is dynamically adapted to 0.5 | |
Categorization of Population | Leaders: Top 40% of the population, tasked with exploring optimal regions using their enhanced knowledge. |
Searchers: Ranked between 40% and 80% of the population, responsible for exploring unknown regions globally. | |
Followers: Ranked between 80% and 90% of the population, mimic leaders for guided exploration. | |
Losers: Bottom 10% of individuals, replaced by newly initialized candidates to maintain diversity. | |
Diversity Factor (DF) | \(\:DF=0.2\times\:rand\), it is adjusted in a dynamic manner to prevent early convergence that ensures the population explores diverse solutions efficiently. |
Enhanced Population Update | Employs Jump Mechanism and Levy Flight for efficacious local and global searches. |
Represents diversity throughout updates utilizing chaotic behavior (Sine Map), preventing early convergence to local optima. |
