Name
Abstract | ||
|---|---|---|
Stable matching-based selection models the selection process in MOEA/D as a stable marriage problem. By finding a stable matching between the subproblems and solutions, the solutions are assigned to subproblems to balance the convergence and the diversity. In this paper, a two-level stable matching-based selection is proposed to further guarantee the diversity of the population. More specifically, the first level of stable matching only matches a solution to one of its most preferred subproblems and the second level of stable matching is responsible for matching the solutions to the remaining subproblems. Experimental studies demonstrate that the proposed selection scheme is effective and competitive comparing to other state-of-the-art selection schemes for MOEA/D. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/SMC.2015.302 | 2015 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC 2015): BIG DATA ANALYTICS FOR HUMAN-CENTRIC SYSTEMS |
Field | DocType | ISSN |
Convergence (routing),Population,Stable marriage problem,Mathematical optimization,Computer science,Multi-objective optimization,Artificial intelligence,Machine learning | Conference | 1062-922X |
Citations | PageRank | References |
6 | 0.43 | 19 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mengyuan Wu | 1 | 62 | 2.71 |
| Sam Kwong | 2 | 4590 | 315.78 |
| Qingfu Zhang | 3 | 7634 | 255.05 |
| Ke Li | 4 | 798 | 29.81 |
| Ran Wang | 5 | 439 | 24.42 |
| Bo Liu | 6 | 7 | 1.14 |