Optimization of artificial flockings by means of anisotropy measurements

TL;DR

This paper presents an optimization method using genetic algorithms to enhance artificial flocking behavior by maximizing anisotropy, leading to more realistic flock simulations, and compares metric and topological interaction models.

Contribution

It introduces a novel optimization approach for artificial flocking parameters based on anisotropy measurements and evaluates interaction models against empirical data.

Findings

Genetic algorithms effectively optimize flocking parameters for high anisotropy.

Topological interaction models better match empirical flocking behavior than metric models.

Optimized parameters improve the realism and efficiency of artificial flock simulations.

Abstract

An effective procedure to determine the optimal parameters appearing in artificial flockings is proposed in terms of optimization problems. We numerically examine genetic algorithms (GAs) to determine the optimal set of parameters such as the weights for three essential interactions in BOIDS by Reynolds (1987) under `zero-collision' and `no-breaking-up' constraints. As a fitness function (the energy function) to be maximized by the GA, we choose the so-called the $\gamma$-value of anisotropy which can be observed empirically in typical flocks of starling. We confirm that the GA successfully finds the solution having a large $\gamma$-value leading-up to a strong anisotropy. The numerical experience shows that the procedure might enable us to make more realistic and efficient artificial flocking of starling even in our personal computers. We also evaluate two distinct types of interactions in agents, namely, metric and topological definitions of interactions. We confirmed that the topological definition can explain the empirical evidence much better than the metric definition does.