| Begin |
| Population initialization |
| Step 1. Set population size CS, the number of food sources , the non-improvement number of food source , |
| the number of maximum iterations , and the number of parameters . |
| Step 2. Generate initial solutions using population initialization based on SSD as in (5) and (6), and calculate their fitness |
| values by (7) and (8). |
| Step 3. cycle = 1 |
| Step 4. While cycle ≤ do |
| Employed bee phase |
| For = 1 : do |
| Update the position of the employed bee based on (3). |
| Check whether it is out of boundaries or not. |
| Calculate fitness value by (7) and (8). |
| A greedy selection strategy is applied to select the better food source. |
| A food source doesn’t improve = + 1, otherwise = 0 where represents |
| the number of the th trial. |
| End for |
| Onlooker bee phase |
| Step 5. Calculate based on (9), and initialize = 0, = 0. |
| While < then do |
| If random < then do |
| Update the position of the onlooker bee by (3). |
| Check whether it is out of boundaries or not. |
| Calculate fitness value by (7) and (8). |
| A greedy selection strategy is applied to select the better food source. |
| A food source doesn’t improve = + 1, otherwise = 0. |
| End if |
| End while ( = ) |
| Scout bee phase |
| Step 6. if > then do |
| Generate a new food source for the scout bee by (10). |
| End if |
| Step 7. Memorize optimal solution achieved till now. cycle = cycle + 1; |
| Step 8. End while (cycle = ) |
| End. |
