摘要
针对粒子群算法易出现早熟,搜索精度低的问题,从惯性权重的确定和算法搜索精度两个方面进行了改进。其中惯性权重由随迭代次数非线性递减函数和一随机扰动项确定,利用这个扰动项的突变性来跳出极小值区域,同时为增加粒子的多样性,提高算法搜索精度,引入了变尺度混沌搜索,并将该方法和标准粒子群算法分别与小波去噪结合,预测地基累计沉降量并做了对比,实验表明本文方法具有良好的全局和局部搜索能力,预测精度高。
Contrary to the problem of premature and low searching precision which the particle swarm optimization （PSO） has, this paper improved it from two aspects： the method of fixing inertia weight and the method of improving the algorithm＇s searching precision. The inertia weight was determined by a function whose value was decreased nordinearly and a stochastic value. The stochastic value randomness to jump out the local optima is used. In order to improve the particle＇ s diversity and the algorithm＇ s ability of searching global optima, the scale chaos searching was introduced. Also we made a comparison with the standard particle swarm optimization （SPSO） with wavelet to forecast foundation settlement. The experiment indicated that the method had strong global and local searching optima and high forecast precision.
出处
《山东大学学报（理学版）》
CAS
CSCD
北大核心
2009年第11期75-78,共4页
Journal of Shandong University(Natural Science)
关键词
小波分析
粒子群优化算法
地基沉降
预测
wavelet analysis
particle swarm optimization
foundation settlement
prediction
