Diversity Guided Immune Clonal Quantum-Behaved Particle Swarm Optimization Algorithm and the Wavelet in the Forecasting of Foundation Settlement

摘要：

In dealing with the problem of the quantum-behaved particle swarm optimization algorithm (QPSO) easy falling into the local optima,we proposed the diversity guided immune clonal QPSO. In this algorithm the swarm was defined two states: attraction and expansion. During the optimization process the swarm transferred between the two states repeatedly reference to the swarm diversity. When in the attraction state if the diversity is less than the pre-established value,we will carry the immune clonal algorithm to do the local searching. And we used this algorithm with wavelet to forecast the foundation settlement,and also made a compare with standard quantum-behaved particle swarm optimization with wavelet. The experiment indicated that this improved method had a better ability of searching global and local optima and high forecasting precision.

关键词： wavelet analysis quantum-behaved particle swarm optimization algorithm immune clonal foundation settlement forecasting.

作者: Jiwen Dong Ruihai Wu

作者单位: School of Information Science and Engineering University of Jinan 106 Jiwei roads,Jinan,Shandong Province,China

会议类型: 国际会议

会议名称: 2009 9th International Conference on Electronic Measurement & Instruments(第九届电子测量与仪器国际会议 ICEMI2009)

会议地点: 北京

会议语种:英文

页码: 2701-2705

在线出版日期: 2009-08-16（万方平台首次上网日期，不代表论文的发表时间）