Generative communication in Linda
ACM Transactions on Programming Languages and Systems (TOPLAS)
SETI@home: an experiment in public-resource computing
Communications of the ACM
A Taxonomy of Hybrid Metaheuristics
Journal of Heuristics
XtremWeb: Building an Experimental Platform for Global Computing
GRID '00 Proceedings of the First IEEE/ACM International Workshop on Grid Computing
A Hybrid Evolutionary Approach for Multicriteria Optimization Problems: Application to the Flow Shop
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Multicriteria Scheduling: Theory, Models and Algorithms
Multicriteria Scheduling: Theory, Models and Algorithms
Considerations in engineering parallel multiobjective evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Peer-to-peer evolutionary algorithms with adaptive autonomous selection
Proceedings of the 9th annual conference on Genetic and evolutionary computation
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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.