| Create an initial population POP, as follow: |
| (i) , |
| Where Min(dataset) and Max(dataset) are correspond with data items that their features are |
| the minimum and maximum values in whole of the dataset respectively. |
| (ii) = Create a candidate solution using the minimum of the dataset |
| (iii) = Create a candidate solution using the maximum of the dataset |
| (iv) = Create a candidate solution using the mean of the dataset |
| (v) : Create all other candidate solutions randomly as follow: |
| for pop |
| for |
| for |
| = random number in range of |
| Where LC() and UC() are the lower and upper bounds for each decision |
| variable (i.e. LC() < < UC(), ). |
| end for |
| end for |
| end for |
| Calculate fitness of POP (cost function) according to (3) and sort it from the Best |
| (minimum) fitness to the worst one (maximum). |
| for to , (for each solution ) |
| Compute emigration rate proportional to fitness of , where , so that , |
| and immigration rate , so that , . |
| end for |
| Set , , , based on (8)–(11) |
| While termination conditions are not met |
| NewPOP ← POP |
| for to pop (for each solution ) |
| for to (for each candidate solution decision variable index ) |
| Choose whether to immigrate with probabilistically decide . |
| if New is selected for immigrating |
| Use values to probabilistically select the emigrating solution . |
| if is selected for emigrating |
| , based on (5) by setting |
| = random number in range of (0.9, 1). |
| end if |
| end if |
| With probabilistically decide whether to mutate based on (6). |
| if is selected for mutation, thus based on (7), (8): |
| for |
| if |
| |
| , where its value in this work |
| is 0.02, also are the lower and upper bounds for each decision variable |
| Break |
| end if |
| end for |
| end if |
| end for (repeat for next candidate solution decision variable index) |
| Recalculate fitness of . |
| End for (repeat for next solution) |
| Sort population based on fitness function. |
| Make new population POP, based on combinatorial of old POP and New POP based on (7). |
| Sort new population based on fitness function. |
| Update and store, best solution ever found. |
| end while |
