Name
Abstract | ||
|---|---|---|
In multi-objective evolutionary algorithms (MOEAs), the diversity of Pareto front (PF) is significant. For good diversity can provide more reasonable choices to decision-makers. The diversity of PF includes the span and the uniformity. In this paper, we proposed a dynamic crowding distance (DCD) based diversity maintenance strategy (DMS) (DCD-DMS), in which individualpsilas DCD are computed based on the difference degree between the crowding distances of different objectives. The proposed strategy computes individualspsila DCD dynamically during the process of population maintenance. Through experiments on 9 test problems, the results demonstrate that DCD can improve diversity at a high level compared with two popular MOEAs: NSGA-II and epsiv-MOEA. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ICNC.2008.532 | ICNC |
Keywords | Field | DocType |
dynamic crowding distance,evolutionary computation,new diversity maintenance strategy,proposed strategy computes individual,diversity maintenance strategy,multiobjective evolutionary algorithm,pareto optimisation,good diversity,difference degree,population maintenance,decision-maker,popular moeas,dcd dynamically,pareto front,crowding distance,measurement,optimization,decision maker,convergence,maintenance engineering,algorithm design and analysis | Convergence (routing),Population,Mathematical optimization,Algorithm design,Evolutionary algorithm,Crowding,Computer science,Evolutionary computation,Multi-objective optimization,Artificial intelligence,Maintenance engineering,Machine learning | Conference |
Volume | ISBN | Citations |
1 | 978-0-7695-3304-9 | 17 |
PageRank | References | Authors |
1.09 | 9 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Biao Luo | 1 | 554 | 23.80 |
| Jinhua Zheng | 2 | 517 | 36.36 |
| Jiongliang Xie | 3 | 17 | 1.77 |
| Jun Wu | 4 | 32 | 2.84 |