Lightweight Evolution Strategies for Nanoswimmers-oriented in Vivo Computation

Shaolong SHI, Yifan CHEN, Xin YAO, Mengjie ZHANG

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

9 Citations (Scopus)

Abstract

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. © 2019 IEEE.
Original languageEnglish
Title of host publication2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages866-872
Number of pages7
ISBN (Print)9781728121536
DOIs
Publication statusPublished - Jun 2019
Externally publishedYes

Bibliographical note

This work was supported by National Key R&D Program of China (Grant No. 2017YFC0804003), the Program for Guangdong Introducing Innovative and Enterpreneurial Teams (Grant No. 2017ZT07X386), Guangdong Natural Science Funds (2016A030313640), Shenzhen Peacock Plan (Grant No. KQTD2016112514355531), the Science and Technology Innovation Committee Foundation of Shenzhen (Grant Nos. ZDSYS201703031748284, JCYJ20160301113918121, JCYJ20170307105521943, JCYJ20170817112421757) and the Program for University Key Laboratory of Guangdong Province (Grant No. 2017KSYS008).

Keywords

  • cancer detection
  • gravitational search algorithm
  • nanoswimmers
  • swarm intelligence
  • uniform magnetic field

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