Name
Abstract | ||
|---|---|---|
With the intrinsic properties of multiobjective optimization problems in mind, multiobjective coevolutionary algorithm (MOCEA) is proposed. In MOCEA, a Pareto crossover operator, and 3 coevolutionary operators are designed for maintaining the population diversity and increasing the convergence rate. Moreover, a crowding distance is designed to reduce the size of the nondominated set. Experimental results demonstrate that MOCEA can find better solutions at a low computational cost. At the same time, the solutions found by MOCEA scatter uniformly over the entire Pareto front. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1007/978-3-540-25929-9_98 | Lecture Notes in Artificial Intelligence |
Keywords | Field | DocType |
multiobjective optimization,pareto front,convergence rate | Crowding distance,Mathematical optimization,Crossover,Computer science,Algorithm,Multi-objective optimization,Population diversity,Operator (computer programming),Rate of convergence,Multiobjective optimization problem,Pareto principle | Conference |
Volume | ISSN | Citations |
3066 | 0302-9743 | 1 |
PageRank | References | Authors |
0.40 | 4 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jing Liu | 1 | 1043 | 115.54 |
| Weicai Zhong | 2 | 381 | 26.14 |
| Licheng Jiao | 3 | 5698 | 475.84 |
| Fang Liu | 4 | 2 | 1.08 |