Name
Abstract | ||
|---|---|---|
A particle swarm optimization algorithm with crossover operation (PSOCO) is proposed. In the proposed PSOCO, two different crossover operations are employed in order to breed promising exemplars. By performing crossover on the personal historical best position of each particle, the effective guiding exemplars are constructed and they maintain a good diversity. In turn, these high quality exemplars are used to guide the evolution of particles. PSOCO is two-layer particle swarm optimization with positive feedback mechanism. In order to test the performance of PSOCO, we use a set of widely used benchmark functions. The experimental results demonstrate that the proposed PSOCO is a competitive optimizer in terms of both solution quality and efficiency. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.engappai.2018.01.009 | Engineering Applications of Artificial Intelligence |
Keywords | Field | DocType |
Swarm intelligence,Particle swarm optimization,Crossover operation,Global optimization | Particle swarm optimization,Mathematical optimization,Crossover,Computer science,Particle,Particle swarm optimizer | Journal |
Volume | ISSN | Citations |
70 | 0952-1976 | 14 |
PageRank | References | Authors |
0.61 | 41 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yonggang Chen | 1 | 267 | 20.44 |
| Lixiang Li | 2 | 533 | 46.82 |
| Jinghua Xiao | 3 | 231 | 18.17 |
| Yixian Yang | 4 | 1121 | 140.62 |
| Jun Liang | 5 | 232 | 19.75 |
| Tao Li | 6 | 143 | 54.36 |