| | Algorithm: CEPO |
| | Input: the emperor penguin population |
| | Output: the optimal position of emperor penguin |
| (1) | Procedure CEPO |
| (2) | Initialize the size of population, , the parameters of EPO and CA |
| (3) | Initialize the situational knowledge and normative knowledge of the belief space |
| (4) | Initialize the population space of Eps |
| (5) | Compute the fitness of each EP |
| (6) | Arrange the fitness value of each EP |
| (7) | Initialize the belief space by acceptance proportion 20% and AdjustCulture |
| (8) | Initialize the belief space |
| (9) | While () do |
| (10) | Calculate and using equations. (9) and (10) |
| (11) | For1 to do |
| (12) | |
| (13) | For1 to do |
| (14) | If using equation (7) |
| (15) | Update the position of EPs using equation (8) |
| (16) | Else |
| (17) | Compute and using equations (12) and (13) |
| (18) | Compute using equation (14) |
| (19) | Compute the new position of EPs using equation (15) |
| (20) | Compute the fitness of current Eps |
| (21) | Update the better position of EP compared with the previous position |
| (22) | End if |
| (23) | End for |
| (24) | End for |
| (25) | Amend EPs which cross the boundary |
| (26) | Arrange the fitness value of each EP |
| (27) | Update the belief space by acceptance proportion top 20% and AdjustCulture |
| (28) | |
| (29) | End while |
| (30) | Return the optimal position |
| (31) | End procedure |
| (32) | Procedure AdjustCulture |
| (33) | Update situational knowledge using equation (2) |
| (34) | For to do |
| (35) | |
| (36) | If |
| (37) | For to do |
| (38) | Update the lower boundary of normative knowledge using equation (3) and (4) |
| (39) | Else |
| (40) | For to do |
| (41) | Update the upper boundary of normative knowledge using equation (5) and (6) |
| (42) | End if |
| (43) | End for |
| (44) | End for |
| (45) | End procedure |
