We introduce a generic framework for the distributed execution of combinatorial optimization tasks. Instead of relying on custom hardware (like dedicated parallel machines or clusters), our approach exploits, in a peer-to-peer fashion, the computing and storage power of existing, off-the- shelf desktops and servers. Contributions of this paper are a description of the generic framework, together with a first instantiation based on particle swarm optimization (PSO). Simulation results are shown, proving the efficacy of our distributed PSO algorithm in optimizing a large number of benchmark functions.
['Marco Biazzini', 'Mauro Brunato', 'Alberto Montresor']
Towards a decentralized architecture for optimization