IEE/CSE 598: Lecture 6B (2020-03-16) - Bacterial Foraging Opt./Intro to Particle Swarm Optimization скачать в хорошем качестве

IEE/CSE 598: Lecture 6B (2020-03-16) - Bacterial Foraging Opt./Intro to Particle Swarm Optimization

In this lecture, we continue our discussion of algorithms in the category of swarm intelligence. We start with a recap of Ant System (AS) – a precursor to Ant Colony Optimization (ACO) with very similar underlying mechanisms of stigmergic operation based on virtual pheromones. We then pivot to describe bacterial foraging optimization (BFO), which considers freely moving bacteria that can "tumble" whenever reaching a nutrient decline and "run" across nutrient increases. Like trail-laying ants, these bacteria can use chemicals to aggregate and spread social information. These chemicals can be attractive or repulsive. Like evolutionary algorithms, these bacteria can be eliminated based on poor local performance and increased in areas based on good local performance. After covering bacterial foraging optimization, we start to introduce particle swarm optimization (PSO), which is a similar motion-based metaheuristic meant to maintain cohesive swarms that move through a decision space. We will pick up on PSO more in the next lecture. Whiteboard lecture notes (with some corrections) can be found at: https://www.dropbox.com/s/2mkcuyl8kzv... This lecture was recorded by Theodore Pavlic as part of IEE/CSE 598 (Bio-Inspired AI and Optimization) at Arizona State University.