IMPROVING GLOBAL OPTIMIZATION ABILITY OF GSO USING ENSEMBLE LEARNING

摘要：

　　As a novel bionic swarm intelligence optimization method,Glowworm Swarm Optimization (GSO) algorithm is inspired by the social behavior of glowworm and the phenomenon of bioluminescent communication,but GSO is easy to fall into local optimization point,and has the low speed of convergence in the late.In order to solve these problems,a method GSOE,combined with the GSO and the ensemble learning method,is presented.Through 4 typical functions testing,experiment results show that the method offers an effective way to avoid local optimization,and can improve the optimization global ability obviously.

关键词： Swarm intelligence Glowworm swarm optimization (GSO) Ensemble learning method

作者: Qin Wang Yan Shi Guangping Zeng Xuyan Tu

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

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: 147-150

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）