A study of hybrid parallel genetic algorithm model

Genetic algorithms is facing the low evolution rate and difficulties to meet real-time requirements when handing large-scale combinatorial optimization problems. In this paper, we propose a coarse-grained-master-slave hybrid parallel genetic algorithm model based on multi-core cluster systems. This model integrates the message-passing model and the shared-memory model. We use message-passing model-MPI among nodes which correspond to coarse-grained Parallel Genetic Algorithm (PGA), meanwhile use share-memory model-OpenMP within the node which correspond to master-slave PGA. So it can combine effectively the higher parallel computing ability of multi-core cluster system with inherent parallelism of PGA. On the basis of the proposed model, we implemented a hybrid parallel genetic algorithm (HPGA) based on two-layer parallelism of processes and threads, and it is used to solve several benchmark functions. Theoretical analysis and experimental result show that the proposed model has superiority in versatility and convenience for parallel genetic algorithm design.