HYBRID ENSEMBLE PSO-GSO ALGORITHM

摘要：

　　A hybrid swarm optimization algorithm is presented which combines particle swarm optimization (PSO) with glowworm swarm optimization (GSO) and in which ensemble learning is used to synthesize the final population.PSO can converge quickly but always falls into premature problem,while GSO is easy to capture many peaks of multimodal function due to its dynamic sub-groups but also easy to be trapped in problems of low convergence and low precision.Combining the two algorithms can balance the diversity and convergence.And ensemble learning could achieve a more accurate position.Experiment results are examined with benchmark functions and results show that the proposed hybrid algorithm outperforms many versions of PSO.

关键词： Particle swarm optimization (PSO) Glowworm swarm optimization (GSO) Ensemble learning

作者: Yan Shi Qin Wang Huiyan Zhang

作者单位: School of Computer & Information Engineering,Beijing Technology and Business University,Beijing 1000 School of Computer & Communication Engineering,University of Science and Technology Beijing,Beijing

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: 143-146

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）