Ricardo M Araujo, Luis C. Lamb
We explore the relationship between properties of the network defined by connected agents and the global system performance. This is achieved by means of a novel class of optimization algorithms. This new class makes explicit use of an underlying network that structures the information flow between multiple agents performing local searches. We show that this new class of algorithms is competitive with respect to other population-based optimization techniques. Finally, we demonstrate by numerical simulations that changes in the way the network is built leads to variations in the system's performance. In particular, we show how constrained hubs — highly connected agents — can be beneficial in particular optimization problems.
Subjects: 7. Distributed AI; 7.1 Multi-Agent Systems
Submitted: Apr 15, 2008