Two extensions of heterogeneous boid model to avoid metastable patterns

Abstract

A boid is a simple multiagent model of animal group behavior. Boid agents communicate locally. I studied a heterogeneous boid model comprised of many agents that are divided into several types. While varying interaction among types of agents, this model generates stable patterns with a symmetric interface among different types of agents. As the number of agents increases and as agent clusters grow larger, this model forms a metastable pattern (i.e., a complex of such stable patterns) that is caused by conflict among the local growths of stable patterns. To avoid metastable patterns, I designed two extended heterogeneous boid models: a two-component boid with noise control and a three-component boid with a type transition of agents. In this paper, I examined how these extended models rearrange agents from metastable patterns into stable patterns. These extended models generate stable patterns regardless of the agent number in a shorter time than the original heterogeneous boid model.