Name
Abstract | ||
|---|---|---|
We have recently proposed a novel framework of in vivo computation by transforming the early tumor detection into an optimization problem. In the framework, the tumor-triggered biological gradient field (BGF) provides aided knowledge for the swarm-intelligence-assisted tumor targeting process. Our previous investigations are based on the hypothesis that the BGF landscape is time-invariant, which results in a static function optimization problem. However, the properties of internal environment, such as the flow state of body fluid, will bring about time-dependent variation of BGF. Thus, we focus on dynamic in vivo computation by considering different variation patterns of BGF in this paper. A computational intelligence strategy named “swarm-based learning strategy” is proposed for overcoming the turbulence of the fitness estimation caused by the BGF variation. The in silico experiments and statistical results demonstrate the effectiveness of the proposed strategy. In addition, the above process is conducted in a three-dimensional search space, which is more realistic compared to the two-dimensional search space in our previous work. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/CEC55065.2022.9870332 | 2022 IEEE Congress on Evolutionary Computation (CEC) |
Keywords | DocType | ISBN |
Tumor targeting,dynamic in vivo computation,nanorobots,swarm intelligence | Conference | 978-1-6654-6709-4 |
Citations | PageRank | References |
0 | 0.34 | 2 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shaolong Shi | 1 | 4 | 3.35 |
| Yifan Chen | 2 | 1 | 3.07 |
| Quan Liu | 3 | 28 | 12.17 |
| Jurong Ding | 4 | 0 | 0.68 |
| Qingfu Zhang | 5 | 2 | 2.39 |