Parallel Hybrid Multi-Objective Island Model in Peer-to-Peer Environment

Solving large size and time-intensive combinatorial optimization problems with parallel hybrid multi-objective evolutionary algorithms (MO-EAs) requires a large amount of computational resources. Peer-to-Peer (P2P) computing is recently revealed as a powerful way to harness these resources and efficiently deal with such problems. In this paper, we focus on the parallel hybrid multi-objective island model for P2P systems. We address its design, implementation, and fault-tolerant deployment in a P2P context. The proposed model have been experimented on the Bi-criterion Permutation Flow-Shop Problem (BPFSP) on a network of 120 heterogeneous PCs. The preliminary results demonstrate the effectiveness of this model and its capabilities to fully exploit the hybridization.