Lecture
This lecture covers the general logistics of the course, including the team involved, course website, Moodle access, lecture format, labs, and homework. It also discusses the course rationale, prerequisites, organization, credits, workload, grading, and course content. The lecture introduces the course syllabus, similarities with previous editions, and the focus on theory, algorithms, and experimental labs. It delves into topics like swarm intelligence, foraging strategies, ant-inspired metaheuristics, and collective phenomena. The lecture also touches on distributed control algorithms, optimization, and environmental sensing. It concludes with suggestions for successful course material processing, labs, exercises, and homework.