Lightweight Evolution Strategies for Nanoswimmers-oriented In Vivo Computation - Shi, Shaolong; Chen, Yifan; Yao, Xin; Zhang, Mengjie (2019 IEEE Congress on Evolutionary Computation (CEC), 2019)
We propose two novel evolution strategies of swarm intelligence for nanoswimmer-oriented in vivo computation, which corresponds to the computing model of the direct targeting strategy (DTS) where externally manipulable magnetic nanoswimmers are employed for cancer detection. In the DTS, the nanoswimmers move in the high-risk tissue region guided by an external magnetic field to search for the early cancer that cannot be visualized using traditional imaging modalities due to their limited resolution. Subject to the constraint of the state-of-the-art controlling technology which can only generate a uniform magnetic field to steer all the nanoswimmers simultaneously, we revisit the conventional gravitational search algorithm (GSA) and propose the orthokinetic gravitational search algorithm (OGSA) to carry out the DTS. Furthermore, we propose the general evolution strategy (G-ES) and the weak priority evolution strategy (WP-ES) and apply them to the OGSA for the path planning of magnetic nanoswimmers. To prove the superiority of the OGSA in the DTS, we present some simulation examples and make comparison with the "brute-force" search, which corresponds to the traditional systemic targeting strategy. Furthermore, we compare the performance of WP-ES and G-ES in the OGSA. It is found that the WP-ES can improve the performance of swarm intelligence algorithms (e.g., GSA) in the DTS.