A Intelligent Nanorobots Fish Swarm Strategy for Tumor Targeting

Abstract

This paper proposes a nanorobots fish swarm algorithm (NFSA) for tumor targeting. The alterations in the tumor microenvironment caused by tumor growth produce the biological gradient field (BGF), which is regulated by the adjacent tortuous and dense capillary network. NFSA is used to measure tumor-targeting efficiency in comparison to the benchmarks of Brute-force and the conventional gradient descent algorithm. Our goal is to increase the efficiency of targeting tumors in the early stages by using existing swarm intelligence algorithms to manipulate nanorobot swarms (NS) through magnetic fields. The extracorporeal observation system sensed the motion of NS under the influence of a BGF and then estimated the gradient of BGF. The invasive percolation algorithm models the vascular network to evaluate the performance of searching strategies. We also apply the exponential evolution step mechanism to boost the tumor-targeting efficiency of NFSA. The results show that NFSA has higher overall tumor targeting efficiency and a fast convergence property than previous algorithms. We hope that the NS in a multi-agent system could pave the way for challenges in tumor targeting.