After a few brief comments about student mini-projects related to multi-objective evolutionary algorithms, this lecture covers Ant Colony Optimization (ACO), with a particular focus on the Ant System (AS) prototype that came before it. This optimization metaheuristic was built originally to solve combinatorial (discrete) optimization problems mimicking how some ants lay pheromonal foraging trails, reinforcing good subgraphs through a network of possible solution candidates to a design problem. The class closes with an introduction to Bacterial Foraging Optimization (BFO), which is inspired by "running and tumbling" flagellated bacteria that can climb local concentration gradients and get out of local traps by using social information. During the class, we also introduce the concept of "stigmergy" (i.e., coordination through modification of the surrounding environment).
Whiteboard lecture notes available at: https://www.dropbox.com/s/shpheh73u1tngyw/IEE598-Lecture6B-2022-03-15-Distributed_AI_and_Swarm_Intelligence-Part_2-ACO_and_Bacterial_Foraging_Optimization.pdf?dl=0
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