Swarm intelligence deals with natural and artificial systems composed of many individuals that coordinate using decentralized control and self-organization, which consequently form an emergent behavior. In particular, the discipline focuses on the collective behaviors that result from the local interactions of the individuals with each other and with their environment. Such systems are highly adaptable, robust, flexible, and capable to function autonomously. The swarm-based mechanisms can be very useful in technical systems as well as in optimization of complex problems.
We focus especially on the concepts of swarm evolution, collective intelligence, stygmergic communication and self-organization, and discuss different swarm intelligence algorithms that belong to the set of probabilistic metaheuristic approaches.
During the course, a number of swarm intelligence systems (bee colonies, ant colonies, schools of fish, flocks of birds, herds of land animals) will be presented together with the corresponding swarm intelligence algorithms that will be analyzed and compared. Emphasis is given to such topics as the modeling and theoretical analysis as well as the real applications from diverse domains to show the usefulness of this approach. The other “accompanied” aspects such as parameters setting and tuning will be also presented.
The following topics will be covered:
Part 1: Fundamentals of swarm intelligence (stability analysis, swarm aggregation, swarm in known and unknown environments, dynamic optimization);
Part 2: Swarm-based metaheuristics (theoretical foundations, swarm clustering and sorting, Particle Swarms, Ant Colony, Artificial Bees, Fireflies Algorithm, Bacterial Foraging, recent advances and new inspirations);
Part 3: Applications (swarm robotics, internet computing, software engineering, sensors, data mining,…) regarding different optimization problems, finding optimal routes, scheduling, routing, structural optimization, image and data analysis.