Research Article
Decomposition-Based Multiobjective Optimization with Invasive Weed Colonies
Algorithm 1
MOEA/D.
| 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 reproduce; /andare selected from/ | |
| 8 mutate; | |
| 9 if then | |
| 10 repair; | |
| 11 end | |
| 12 foreach i to do | |
| 13 if then | |
| 14 ; | |
| 15 end | |
| 16 end | |
| 17 foreach do | |
| 18 if then | |
| 19 ; | |
| 20 ; | |
| 21 end | |
| 22 end | |
| 23 end | |
| 24 end |