| | Definition: |
| | : the population size; |
| | : the maximum number of generations for stopping criterion; |
| | : dimension of the problem; |
| | : the decision matrix with the size of ; |
| | : the function value vector with the size of 1; |
| | : the learning rate for conventional mutation strategy. |
| (1) | BEGIN |
| (2) | Set mutation probability and learning rate ; |
| (3) | Create a randomly initialized population {}; |
| (4) | Let ; |
| (5) | while do |
| (6) | while do |
| (7) | Locate in X and obtain its nearest superior neighbors and inferior neighbors , with ; |
| (8) | Select as the tournament best from () and as the tournament worst from (), with ; |
| (9) | Select as the ; |
| (10) | Generate three random values , , and , where , , and ; |
| (11) | Compute the convex combination vector weights , , and , according to ; |
| (12) | Obtain the combination vector: ; |
| (13) | Generate the random values , , and , where , , and again; |
| (14) | Compute the learning rates , , and , according to ; |
| (15) | ifthen |
| (16) | The triangular mutation vector ; |
| (17) | else |
| (18) | The conventional mutation vector ; |
| (19) | end if |
| (20) | Repair if it violates the upperbound or lowerbound; |
| (21) | Generate ; |
| (22) | whiledo |
| (23) | if or then |
| (24) | ; |
| (25) | else |
| (26) | ; |
| (27) | end if |
| (28) | end while |
| (29) | if then |
| (30) | ; |
| (31) | if then |
| (32) | ; |
| (33) | end if |
| (34) | else |
| (35) | ; |
| (36) | end if |
| (37) | end while |
| (38) | end while |
| (39) | Return; |
| (40) | END |
