Name
Abstract | ||
|---|---|---|
GSA (Gravitational Search Algorithm) is inspired by the Newton's law of universal gravitation and considered as a promising evolutional algorithm, which has the advantages of easy implementation, fast convergence, and low computational cost. However, GSA has the disadvantages that its convergence speed slows down in the later search stage and it is easy to fall into local optimum solution. We proposed a novel immunity-based Gravitational Search Algorithm (IGSA) that is inspired by the biological immune system and the traditional gravitational search algorithm. The comparison experiments of GSA, IGOA and PSO (Particle Swarm Optimization) on 5 benchmark functions are carried out. The proposed algorithm shows competitive results with improved diversity and convergence speed. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-34062-8_98 | ICICA (LNCS) |
Keywords | Field | DocType |
convergence speed,particle swarm optimization,later search stage,easy implementation,traditional gravitational search algorithm,promising evolutional algorithm,benchmark function,gravitational search algorithm,proposed algorithm,immunity-based gravitational search algorithm,fast convergence,artificial immune system,evolution | Particle swarm optimization,Convergence (routing),Newton's law of universal gravitation,Artificial immune system,Mathematical optimization,Local optimum,Computer science,Biological immune system,Algorithm,Gravitational search algorithm | Conference |
Citations | PageRank | References |
1 | 0.35 | 3 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yu Zhang | 1 | 11 | 2.73 |
| Yana Li | 2 | 1 | 0.35 |
| Feng Xia | 3 | 4 | 1.19 |
| Ziqiang Luo | 4 | 2 | 4.47 |