摘要
针对目前优化爆破震动参数存在的问题,结合矿山实际工程爆破测震数据,分别采用最小二乘法与粒子群算法对测震数据进行了分析处理,得到粒子群算法处理结果的残差绝对值之和为0.870,相对于最小二乘法的2.188减小了1.318,说明粒子群算法在小样本寻优过程中具有技术优势。
In light of the problems in the optimization of blasting vibration parameters, the blasting vibration data measured in a certain mine was processed and analyzed respectively with least square method and particle swarm optimization algorithm. The absolute value sum of residuals of the results obtained by particle swarm optimization algorithm was 0. 870 which was 1. 318 less than that being 2. 188 obtained by least square method, this showed that particle swarm optimization algorithm has technical advantage in small sample optimization.
出处
《矿业研究与开发》
CAS
北大核心
2012年第1期103-105,116,共4页
Mining Research and Development
基金
国家重点基础研究发展计划项目(2011cb411913)
关键词
爆破震动
肖维勒准则
粒子群算法
最小二乘法
Vibration of blasting, Chauv - enet standard, Particle swarm optimization algorithm, Least square method
