| Input: Number of particles in , the maximum generation is , |
| number if inner iterations . |
| Output: The approximate in global repository |
| (1) Decompose the objectives according to Algorithm 1. |
| (2) Randomly initialize the position of each particle; |
| initialize the velocity , . |
| (3) Store the nondominated particles into the subgroup repository ; |
| Update . |
| (4) Initialize the unification factor ; calculate entropies for each subgroup. |
| (5) Initialize each particle’s best position . |
| (6) while do |
| (7) for = 1 : do |
| (8) (a) Update the velocity and position according to the unified particle |
| swarm optimization, the selection procedures of and |
| follow the algorithm in Section 5.2. |
| (9) (b) If the particles’ position beyond the decision space, the corresponding |
| variable should be replaced by the value of the boundaries. |
| (10) (c) Evaluate the new cost of each particle in . |
| (11) (d) Update , add new nondominated particles in and move |
| the dominated solutions out. If the number of particles exceed |
| the capacity of , filter the according to dominance rank. |
| (12) (e) Update . If the number of particles exceed |
| the capacity of , filter the with crowding reference. |
| (13) (f) Calculate , according to Equation (6) and Equation (7). |
| (14) (g) . |
| (15) end for |
| (16) Regroup the objectives according to Algorithm 1. |
| (17) Recompose the subgroup repositories. |
| (18) end while |
