By Harpreet Kaur

Developing behavioral policies designed to efficiently solve target-search problems is a crucial issue both in nature and in the nanotechnology of the 21st century.
In our paper, we address this by characterizing the target-search strategies of simple microswimmers in a homogeneous environment containing sparse targets of unknown positions.

These tiny agents possess the ability of controlling their dynamics by switching between passive and active Brownian motion and by selecting the time duration of each of the two phases. Using a Genetic Algorithm known as NeuroEvolution of Augmenting Topologies, The study unveiled that the magnitude of the particle's self-propulsion during the active phase significantly influences the optimal policy and that a broad spectrum of network topology solutions exists, differing in the number of connections and hidden nodes.

For more information: https://iopscience.iop.org/article/10.1088/2632-2153/ace6f4