Lecture 6D (2022-03-22): Distributed AI & Swarm Intelligence, Part 4 - Particle Swarm Optimization (PSO)

In this lecture, we cover the canonical Particle Swarm Optimization (PSO). We start with its functional motivations from the metaheuristic optimization of artificial neural networks and the mechanistic motivations from flocking graphics models ("boids") and related collective motion models from physics (Vicsek model). We then describe the motion rules for PSO and then briefly discuss more modern variants of PSO that have adaptive parameters (as in adaptive inertia) and use network effects to balance exploration and exploitation (e.g., "LBEST" versions of PSO).

Whiteboard lecture notes for this lecture can be found at: https://www.dropbox.com/s/m6b69fy8b695qgm/IEE598-Lecture6D-2022-03-22-Distributed_AI_and_Swarm_Intelligence-Part_4-Particle_Swarm_Optimization_PSO_and_friends.pdf?dl=0