PARALLEL PARTICLE SWARM OPTIMIZATION WITH GENETIC COMMUNICATION STRATEGY AND ITS IMPLEMENTATION ON GPU

摘要：

　　Taking into account the advantage of high computation to communication ratio of coarse-grained parallel model,we implement coarse-grained parallel particle swarm optimization (PPSO) on Graphic Processing Unit (GPU),which is very popular for parallel computing nowadays.Meanwhile,a heuristic communication strategy called genetic migration is proposed in this paper.Numerical experimental results show that PPSO with genetic migration (PPSO_GM) can greatly improve the convergence property of particle swarm optimization (PSO),compared with PPSO with traditional unidirectional ring migration (PPSO_URM); and two orders of magnitude more speedups are achieved by PPSO_GM against serial PSO (SPSO) for all ten 100-dimensional benchmark test functions.

关键词： Parallel particle swarm optimization Communication strategy Unidirectional ring migration GPU CUDA

作者: Min Jin Huaxiang Lu

作者单位: Artificial Neural Networks Laboratory,Institute of Semiconductors,Chinese Academy of Sciences,Beijing 100083,China

会议类型: 国际会议

会议名称: 2012 2nd IEEE International Conference on Cloud Computing and Intelligence Systems (2012年第2届IEEE云计算与智能系统国际会议(IEEE CCIS2012))

会议地点: 杭州

会议语种:英文

页码: 128-133

在线出版日期: 2012-10-30（万方平台首次上网日期，不代表论文的发表时间）