Bird colony behavior based multi-agent model design and simulation

Abstract

Multi-agent modeling and system theory research has made significant strides forward, in areas as diverse as natural ecosystems, engineering systems and autonomous technologies. This research seeks to delve into the fascinating world of multi-agent models and algorithms, specifically with respect to simulating bird group behavior for exploration purposes. This dissertation opens with an introduction to the history and development of multi-agent modeling, systems theory and collective behavior in natural systems. This dissertation describes the basic concept and advantages of multi-agent systems as well as providing a framework to model collective behavior. Second, by studying the behavior trajectories of birds and collecting and simulating data from natural systems as well as conducting experiments designed around these birds' behavior trajectories, an algorithm was implemented into Python for simulation and verification of this multi-agent model of their behaviour trajectories in Boids multiagent model. The application of bird swarm optimization algorithm to solve the optimization problem is emphasized. The basic principle and implementation method are introduced in detail. Experimental simulations are performed in Python to verify correctness. Overall, this research contributes to our knowledge about multi-agent models and algorithms, illustrates their beauty while simulating bird colony behavior, and showcases multi-agent systems' potential to offer insights and solutions in complex, dynamic environments.