Name
Abstract | ||
|---|---|---|
Evolutionary Algorithm (EA) is a population-based metaheuristic technique to effectively solve Multiobjective Optimization Problem (MOP). However, it is still an active research topic how to improve the performance of MOEA algorithms. In this paper, we present a new FOPF algorithm,which can alleviate MOEA’s disadvantage on time performance. First, a fast obtaining Pareto front approach with less computation cost is proposed, then an expand approach and a limited crossover procedure are employed to keep the diversity of solutions. Experimental results on four test problems show that the FOPF algorithm is able to find solutions with good diversity, which are near the true Parato-optimal front, and improves significantly time performance compared to the known NSGA2. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ICNC.2009.199 | ICNC |
Keywords | DocType | Citations |
data mining,evolutionary computation,neodymium,evolutionary algorithm,algorithm design and analysis,optimization,probability density function,pareto analysis | Conference | 1 |
PageRank | References | Authors |
0.39 | 10 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Song Yang | 1 | 2 | 2.78 |
| Junzhong Ji | 2 | 222 | 29.30 |
| Yamin Wang | 3 | 85 | 4.83 |
| Chunnian Liu | 4 | 561 | 61.58 |