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.

[1]  Paolo Cremonesi,et al.  Parallel, distributed and network-based processing , 2006, J. Syst. Archit..

[2]  Xue Shengjun,et al.  The Analysis and Research of Parallel Genetic Algorithm , 2008, 2008 4th International Conference on Wireless Communications, Networking and Mobile Computing.

[3]  Bernhard Thomaszewski,et al.  Parallel techniques for physically based simulation on multi-core processor architectures , 2008, Comput. Graph..

[4]  Wu Hao.  A Multigroup Parallel Genetic Algorithm Based on Simulated Annealing Method , 2000 .

[5]  Salman Yussof,et al.  A Coarse-Grained Parallel Genetic Algorithm with Migration for Shortest Path Routing Problem , 2009, 2009 11th IEEE International Conference on High Performance Computing and Communications.

[6]  Zdenek Konfrst,et al.  Parallel Genetic Algorithms: Advances, Computing Trends, Applications and Perspectives , 2004, IPDPS.

[7]  Erick Cantú-Paz,et al.  A Survey of Parallel Genetic Algorithms , 2000 .

[8]  Guan Yu.  Parallel Genetic Algorithms with Schema Migration , 2003 .

[9]  Dong Li,et al.  Hybrid MPI/OpenMP power-aware computing , 2010, 2010 IEEE International Symposium on Parallel & Distributed Processing (IPDPS).

[10]  J. Arunadevi,et al.  Intelligent Transport Route Planning Using Parallel Genetic Algorithms and MPI In High Performance Computing Cluster , 2007, 15th International Conference on Advanced Computing and Communications (ADCOM 2007).

[11]  Georg Hager,et al.  Hybrid MPI/OpenMP Parallel Programming on Clusters of Multi-Core SMP Nodes , 2009, 2009 17th Euromicro International Conference on Parallel, Distributed and Network-based Processing.

[12]  Zheng Li-ping,et al.  Master-Slave Parallel Genetic Algorithm Framework on MPI , 2004 .

[13]  San Ye,et al.  Improvement of Real-valued Genetic Algorithm and Performance Study , 2007 .

[14]  刘弹,et al.  Immune clonal selection optimization method with combining mutation strategies , 2007 .

[15]  Dan Liu,et al.  Immune Clonal Selection Optimization Method with Mixed Mutation Strategies , 2007, 2007 Second International Conference on Bio-Inspired Computing: Theories and Applications.

[16]  Lai Xin.  Parallel Genetic Algorithms with Migration Scheme Based on Penetration Theory , 2005 .