This lecture continues our introduction to Swarm Intelligence algorithms, with a major focus on Particle Swarm Optimization (PSO) and the models of collective motion that influenced it (like Reynolds' Boids model for computer graphics, Vicsek self-propelled particles model, and "selfish herds" in general). We contrast PSO with more "stigmergic" optimization algorithms like ant colony optimization (ACO) and bacterial foraging optimization (BFO) and view this more as an algorithm that tries to maintain cohesion of a group of individuals searching for good positions within a space.
Whiteboard notes for this lecture can be found at:
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