A Novel Hybrid Algorithm Based On Baldwinian Learning and PSO

摘要：

In the paper, a novel hybrid algorithm based on Baldwinian learning and PSO (BLPSO) is proposed to increase the diversity of the particles and to prevent premature convergence of PSO. Firstly, BLPSO adopts the Baidwinian operator to simulate the learning mechanism among the particles and employs the information of the swarm to alter the search space adaptively. Secondly, a mutation operation is introduced to make the particles leap the local optimum and enhance the chance to find out the global optimum. Finally, the proposed BLPSO is used to solve some complex optimization problems, the experiment results illustrate the efficiency of the proposed method.

关键词： Hybrid algorithm Baldwinian learning Particle swarm optimization

作者: Wanliang Wang Lili Chen Jing Jie Haiyan Wang Xinli Xu

作者单位: College of Computer Science and Technology Zhejiang University of Technology, ZJUT Hangzhou, P.R.Chi College of Information Engineering Zhejiang University of Technology, ZJUT Hangzhou, P.R.China

会议类型: 国际会议

会议名称: International Conference on Computational Aspects of Social Networks(国际社会网络计算会议 CASoN 2010)

会议地点: 太原

会议语种:英文

页码: 299-302

在线出版日期: 2010-09-26（万方平台首次上网日期，不代表论文的发表时间）