Fig. 1: Neuroevolution of decentralized decision-making in N-bead swimmers.
a Schematics of an N-bead microswimmer environment, with (b) functionally identical yet operationally independent Artificial Neural Networks (ANNs) acting as decentralized decision-making centers (or controllers) to update the respective internal states of the beads, si → si + Δsi (red arrows), and to apply bead-specific forces, Fi ∈ [−2F0, 2F0] (green arrows; ensuring ∑iFi = 0), such that the entire microswimmer self-propels purely based on local perception-action cycles of the constituting bead controllers. c The training progress of optimizing various N-bead microswimmer locomotion policies of type A (see the “Modeling system-level decision-making with decentralized controllers” subsection in the “Results and Discussion”), respectively identifying for predefined values of (N = 3 − 100) the parameters of the morphology-specific ANN controllers via evolutionary algorithms (EAs). The fitness score for different N, quantifying a specific N-bead center of mass velocity \(\bar{v}\) (see the “Modeling system-level decision-making with decentralized controllers” subsection in the “Results and Discussion”), is presented over 200 subsequent generations. Opaque-colored areas below the fitness trajectories indicate the corresponding STD of 10 independent EA searches per morphology and serve as a measure for convergence for the optimization process.
