Adaptive active Brownian particles searching for targets of unknown positions

TL;DR

This study investigates how microswimmers adapt their search strategies for unknown targets using neural networks and evolutionary algorithms, revealing optimal behaviors depend on propulsion strength and network complexity.

Contribution

It introduces an adaptive control framework combining neural networks and genetic algorithms to optimize microswimmer search strategies in unknown environments.

Findings

Optimal search policies depend on propulsion magnitude.

Diverse neural network topologies can achieve effective search behaviors.

Evolutionary algorithms improve target detection efficiency.

Abstract

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. Here, we characterize the target-search strategies of simple microswimmers in a homogeneous environment containing sparse targets of unknown positions. The microswimmers are capable of controlling their dynamics by switching between Brownian motion and an active Brownian particle and by selecting the time duration of each of the two phases. The specific conduct of a single microswimmer depends on an internal decision-making process determined by a simple neural network associated with the agent itself. Starting from a population of individuals with random behavior, we exploit the genetic algorithm NeuroEvolution of Augmenting Topologies to show how an evolutionary pressure based on the target-search performances of single individuals helps to find the optimal duration of the two different phases. Our findings reveal that the optimal policy strongly depends on the magnitude of the particle's self-propulsion during the active phase and that a broad spectrum of network topology solutions exists, differing in the number of connections and hidden nodes.