Review on Particle Swarm Optimization: Application Toward Autonomous Dynamical Systems

摘要

Complex autonomous dynamical systems require sophisticated optimization methods that encompass environment awareness, path planning, and decision-making. swarm intelligence algorithms, inspired by natural phenomena such as bird flocks and fish schools, have undergone significant advancements over recent decades. This paper provides a comprehensive review of particle swarm optimization (PSO) in the context of autonomous systems. We specifically examine the application of PSO to multi-agent dynamical systems, reviewing how PSO variants are employed to tackle diverse optimization challenges across various platforms, including ground vehicles, autonomous under-water vehicles, and unmanned aerial vehicles. Additionally, we delve into the use of PSO within swarm robotics and multi-agent systems. The paper concludes with an outline of potential future research directions, particularly focusing on the application of PSO to the multi-agent rendezvous problem in autonomous systems.