A Novel Computational Nanobiosensing Approach to Improve the Exploitation of In Vivo Computation

A novel nanobiosensing framework named “in vivo computation” has been proposed recently, where the challenge of early tumor detection is overcome from an optimization perspective. The biological gradient field (BGF) triggered by the tumor lesion is viewed as the optimizable objective function with the tumor site being the global optimum. The externally manipulable nanorobots playing the role of agents are manipulated in the search space (i.e., the vascular network of high-risk tissue). Several computational strategies have been proposed to realize tumor targeting by overcoming the in vivo constraints which focused on the tumor detection process without any emphasis on the nanorobots aggregation at the tumor. In this paper, we focus on the utilization rate of agents, which means to improve the percentage of nanorobots that detect the tumor site after it has been found by the first arrival agent (i.e., the nanorobot that detects the tumor at the earliest), and the solution set search, which means to find the tumor region as whole as possible. This process is interpreted as the exploitation process of in vivo computation. An exploitation approach named center-aided weak priority evolution strategy (CWP-ES) is developed for the setting of nanorobot moving direction in this paper. In the approach, a direction generated by the center of agents that have found the tumor mixed with the direction generated by the weak priority evolution strategy (WP-ES) proposed in the previous work is used to steer the motion of nanorobots that have not detected the tumor. Several numerical experiments are performed in a 3D search space to show the effectiveness of this novel computational nanobiosensing approach in three BGF landscapes with different degrees of optimization complexity.