Research Article
Decomposition-Based Multiobjective Optimization with Invasive Weed Colonies
Algorithm 2
MOEA/D-IWO.
| Input: : population size; | |
| : the number of neighbors for each weight vector, ; | |
| : a set of evenly distributed weight vectors; | |
| Output: Pareto solutions on the objective space: | |
| 1 Initialization: | |
| 2 are randomly sampled from , ; | |
| 3 foreach to do ; / are the closest weight vectors to / | |
| 4 reference point . // | |
| 5 while stop criteria are not met do | |
| 6 for to do | |
| 7 IWO; | |
| 8 IWO; | |
| 9 / is selected from / | |
| 10 foreach do | |
| 11 if then | |
| 12 Repair; | |
| 13 end | |
| 14 foreach to do | |
| 15 if then | |
| 16 ; | |
| 17 end | |
| 18 end | |
| 19 foreach do | |
| 20 if then | |
| 21 ; | |
| 22 ; | |
| 23 end | |
| 24 end | |
| 25 end | |
| 26 end | |
| 27 end |