Name
Abstract | ||
|---|---|---|
For many years, evolutionary computation researchers have been trying to extract the swarm intelligence from biological systems in nature. Series of algorithms proposed by imitating animals' behaviours have established themselves as effective means for solving optimization problems. However these bio-inspired methods are not yet satisfactory enough because the behaviour models they reference, such as the foraging birds and bees, are too simple to handle different problems. In this paper, by studying a more complicated behaviour model, human's social behaviour pattern on Twitter which is an influential social media and popular among billions of users, we propose a new algorithm named Twitter Optimization (TO). TO is able to solve most of the real-parameter optimization problems by imitating human's social actions on Twitter: following, tweeting and retweeting. The experiments show that, TO has a good performance on the benchmark functions. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-46675-0_38 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
Swarm Intelligence,Social Media,Twitter Optimization,Particle Swarm Optimization | Particle swarm optimization,Social actions,Social media,Social behaviour,Computer science,Swarm intelligence,Evolutionary computation,Algorithm,Artificial intelligence,Optimization problem,Foraging,Machine learning | Conference |
Volume | ISSN | Citations |
9949 | 0302-9743 | 2 |
PageRank | References | Authors |
0.37 | 3 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| ZhiHui Lv | 1 | 2 | 0.37 |
| Shen Furao | 2 | 515 | 43.27 |
| Jinxi Zhao | 3 | 59 | 6.81 |
| Tao Zhu | 4 | 82 | 14.36 |