Name
Abstract | ||
|---|---|---|
Many-objective optimization problems (MaOPs) pose a big challenge to the traditional Pareto-based multiobjective evolutionary algorithms (MOEAs). As the number of objectives increases, the number of mutually nondominated solutions explodes and MOEAs become invalid due to the loss of Pareto-based selection pressure. Indicator-based many-objective evolutionary algorithms (MaOEAs) have been proposed ... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TCYB.2019.2960302 | IEEE Transactions on Cybernetics |
Keywords | DocType | Volume |
Sociology,Statistics,Convergence,Evolutionary computation,Diversity reception,Optimization,Computational complexity | Journal | 51 |
Issue | ISSN | Citations |
9 | 2168-2267 | 1 |
PageRank | References | Authors |
0.35 | 45 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhengping Liang | 1 | 106 | 8.81 |
| Tingting Luo | 2 | 1 | 0.35 |
| Kaifeng Hu | 3 | 12 | 1.12 |
| Xiaoliang Ma | 4 | 182 | 18.51 |
| Zexuan Zhu | 5 | 989 | 57.41 |