In Vivo Computing Strategies for Tumor Sensitization and Targeting

Shaolong Shi1,2; Yifan Chen1,3; Xin Yao4,5

2020-10-29

10.1109/TCYB.2020.3025859

IEEE Transactions on Cybernetics   影响因子和分区

2168-2275

2168-2275

Several evolution strategies for in vivo computation are proposed with the aim of realizing tumor sensitization and targeting (TST) by externally manipulable nanoswimmers. In such targeting systems, nanoswimmers assembled by magnetic nanoparticles are externally manipulated to search for the tumor in the high-risk tissue by a rotating magnetic field produced by a coil system. This process can be interpreted as in vivo computation, where the tumor in the high-risk tissue corresponds to the global maximum or minimum of the in vivo optimization problem, the nanoswimmers are seen as the computational agents, the tumor-triggered biological gradient field (BGF) is used for fitness evaluation of the agents, and the high-risk tissue is the search space. Considering that the state-of-the-art magnetic nanoswimmer control method can only actuate all the nanoswimmers heading in the same direction simultaneously, we introduce the orthokinetic movement strategies into the agent location updating in the existing swarm intelligence algorithms. Especially, the gravitational search algorithm (GSA) is revisited and the corresponding in vivo optimization algorithm called orthokinetic GSA (OGSA) is proposed to carry out the TST. Furthermore, to determine the direction of the orthokinetic agent movement in every iteration of the operation, we propose several strategies according to the fitness ranking of the nanoswimmers in the BGF. To verify the superiority of the OGSA and choose the optimal evolution strategy, some numerical experiments are presented and compared with that of the brute-force search, which represents the traditional method for TST. It is found that the TST performance can be improved by the weak priority evolution strategy (WP-ES) in most of the scenarios

Evolution strategy magnetic field control magnetic nanoswimmers swarm intelligence tumor sensitization and targeting (TST)

SCI ; EI

英语

其他

Automation & Control Systems ; Computer Science

Automation & Control Systems ; Computer Science, Artificial Intelligence ; Computer Science, Cybernetics

WOS:000819019200085

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC

20222812340689

Biology ; Global optimization ; Iterative methods ; Magnetic fields ; Nanomagnetics ; Nanoparticles ; Numerical methods ; Tumors

Biological Materials and Tissue Engineering:461.2 ; Biology:461.9 ; Magnetism: Basic Concepts and Phenomena:701.2 ; Artificial Intelligence:723.4 ; Nanotechnology:761 ; Optimization Techniques:921.5 ; Numerical Methods:921.6 ; Solid State Physics:933

人工提交

1.School of Life Science, Technology, University of Electronic Science and Technology of China, Chengdu 610051, China
2.Robotics Research Center, Peng Cheng Laboratory, Shenzhen 518055, China
3.School of Engineering, University of Waikato, Hamilton 3216, New Zealand
4.Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
5.School of Computer Science, University of Birmingham, Birmingham B15 2TT, U.K.

Shaolong Shi,Yifan Chen,Xin Yao. In Vivo Computing Strategies for Tumor Sensitization and Targeting[J]. IEEE Transactions on Cybernetics,2020,52(6):4970-4980.

Shaolong Shi,Yifan Chen,&Xin Yao.(2020).In Vivo Computing Strategies for Tumor Sensitization and Targeting.IEEE Transactions on Cybernetics,52(6),4970-4980.

Shaolong Shi,et al."In Vivo Computing Strategies for Tumor Sensitization and Targeting".IEEE Transactions on Cybernetics 52.6(2020):4970-4980.