A Hybrid Particle Swarm Optimization for Numerical Optimization

摘要：

Particle Swarm Optimization (PSO) has shown its good performance on numerical function problems. However, on some multimodal functions the PSO easily suffers from premature convergence because of the rapid decline in velocity. This paper presents a hybrid PSO for numerical optimization, namely HPSO, which employs opposition-based learning (OBL) and a modified velocity model. The OBL provides more chances to find solutions more closely to the global optimum. And the modified velocity model guarantees a non-zero velocity to help trapped particles jump out local minima. Experimental results on 6 benchmark functions show that the HPSO outperforms the standard PSO and opposition-based PSO in all test cases.

关键词： Particle swarm optimization function optimization numerical optimization

作者: Zhengang Ning Liyan Ma Zhenping Li Wenjian Xing

作者单位: School of Information & Electronic Engineering, Hebci University of Engineering, Handan 056038, Chin The Modern Education Technology Center, Hebei University of Engineering, Handan 056038, China

会议类型: 国际会议

会议名称: The Second International Conference on Business Intelligence and Financial Engineering(BIFE 2009)(第二届商务智能与金融工程国际会议)

会议地点: 北京

会议语种:英文

页码: 92-96

在线出版日期: 2009-07-24（万方平台首次上网日期，不代表论文的发表时间）