Name
Abstract | ||
|---|---|---|
This paper proposed an efficient artificial immune network (EaiNet) for function optimization with the guide of spirit of particle swarm optimization (PSO). On the one hand, this algorithm absorbs the learning mechanism of PSO, i.e., the elite learning that each individual is capable of learning from the best in the social population. The introduction of the elite learning quickens the convergence speed of EaiNet. On the other hand, EaiNet has self-learning capability, especially when it is stick in the local optima, which will result in finer global optima. Compared to the conventional artificial immune network (aiNet), EaiNet proposed in this paper has better solution quality and faster convergence speed, which indicates that EaiNet is an effective optimization method. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/ICNC.2007.190 | ICNC |
Keywords | DocType | Volume |
particle swarm optimization,convergence speed,efficient artificial immune network,conventional artificial immune network,local optimum,finer global optimum,elite learning,function optimization,effective optimization method,better solution quality,artificial immune systems,learning artificial intelligence | Conference | 4 |
ISSN | ISBN | Citations |
2157-9555 | 0-7695-2875-9 | 8 |
PageRank | References | Authors |
0.69 | 3 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhonghua Li | 1 | 34 | 4.59 |
| Yunong Zhang | 2 | 2344 | 162.43 |
| Hong-Zhou Tan | 3 | 196 | 33.88 |