| (1) Begin |
| (2) Set the selection probability of each crossover operator to be the same, that is, , |
| where is the sum of all mutation operators. |
| (3) Set the iteration counter and the . |
| (4) Use the Diversification method to generate a population , evaluate each solution in and then set the best solution |
| found so far to be the best one in . |
| (5) while (the termination criterion is not reached) do |
| (6) Use the Reference update method to update reference set . |
| (7) Set , and . Set the solution set to be empty. |
| (8) if () do |
| (9) while () do // generate a new solution for each in |
| (10) Set the successful count and failure count of the crossover operator to be zero. |
| (11) (1) Mutation |
| (12) Randomly select a mutation operator (namely ) using the Adaptive selection method. |
| (13) Randomly select solutions from according to the requirement of mutation operator . |
| (14) Perform mutation operator on the selected solutions to generate a new trial solution . |
| (15) (2) Crossover |
| (16) Apply crossover operation based on (5) to obtain new solution . |
| (17) (3) Selection |
| (18) Use (7) to select the new solution . If , set = ; otherwise, set = . |
| (19) Add and of each crossover operator to the end of a list whose length is . If , |
| then remove the first node of this list so that only a maximum of nodes are stored in list . |
| (20) Update the selection probability of each operator according to the method described in Section 3.4. |
| (21) Set . |
| (22) end while |
| (23) if (the best solution in is better than ) do |
| (24) Update and set . |
| (25) else do |
| (26) Set . |
| (27) end if |
| (28) else do |
| (29) Sort the solutions in the in the ascending order of the objective value, and then randomly reinitialize |
| the latter half of solutions in . |
| (30) Set . |
| (31) end if |
| (32) end while |
| (33) Report the best solution . |
| (34) End |
