| Step 1. Load function and its parameters |
| Step 2. Generate initial population randomly |
| Step 3. While |
| > max number of iterations, |
| performing random global search using Levy Flight , |
| (, , and step length set as ) |
| Then, find a promising solution |
| Step 4. Determine a random number. Set switching parameter for controlling |
| between global search and local search. (We set ) |
| If |
| switch to local search stage (go to Step 5) |
| else |
| switch to global search stage (go to Step 6) |
| Step 5. In intensive local search stage, search around a promising solution, |
| Calculate new velocity and position of each particle via (5) and (6), |
| Then evaluate new fitness (Use the objective function based on Newton–Raphson |
| power flow for reactive power optimization problem) |
| If |
| |
| Step 6. Update, |
| |
| Step 7. Stopping criterion, |
| Maximum number of iterations or a given tolerance (tolerance set as |
| for reactive power optimization problem) |
| Step 8. If any criterion is provided, then stop the algorithm else go to Step |
