Research Article
An Improved Grey Wolf Optimization Algorithm with Variable Weights
Table 1
Pseudocode of the GWO algorithm.
| Description | Pseudocode |
| Set up optimization | Dimension of the given problems | Limitations of the given problems | Population size | Controlling parameter | Stop criterion (maximum iteration times or admissible errors) |
| Initialization | Positions of all of the grey wolves including α, β, and δ wolves |
| Searching | While not the stop criterion, calculate the new fitness function | Update the positions | Limit the scope of positions | Refresh α, β, and δ | Update the stop criterion | End |
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