Lecture 6C (2022-03-17): Distributed AI and Swarm Intelligence, Part 3 - BFO and Intro to Classical Particle Swarm Optimization (PSO) & Its Motivations

This lecture outlines the structure of the Bacterial Foraging Optimization (BFO) metaheuristic for engineering design optimization (EDO) problems. BFO shares some features with other Swarm Intelligence/Distributed AI algorithms, such as Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO), while also borrowing ideas from population-based evolutionary metaheuristics (such as the Genetic Algorithm (GA)). BFO is based on the "run" and "tumble" chemotactic movement of E. coli bacteria, which sense local nutrient concentration gradients as well as chemical communication from other E. coli. After we cover BFO, we pivot to introducing Particle Swarm Optimization (PSO), a more popular swarm-based optimization metaheuristic that moves in virtual space (similar to BFO) but has an inertial component that allows each particle to build up momentum and thus be resistant to sudden changes. We will discuss PSO in more detail in the next lecture.

Whiteboard notes for this lecture can be found at: https://www.dropbox.com/s/kh5ixieys70v2ib/IEE598-Lecture6C-2022-03-17-Distributed_AI_and_Swarm_Intelligence-Part_3-BFO_and_Intro_to_Classical_PSO.pdf?dl=0