基于IABC优化LSSVR的变形预测研究

针对最小二乘支持向量回归机(LSSVR)中惩罚参数c和核函数参数σ难以确定,以及标准人工蜂群算法(ABC)易陷入局部最优、收敛速度慢等问题,提出一种改进的人工蜂群算法(IABC)来优化LSSVR的参数并进行变形预测研究。首先,IABC算法利用反向学习策略生成正反2个种群来增加初始群体的多样性,一次迭代后对双种群的当前最优食物源进行信息交换以实现优中选优,并设计食物源自适应权重函数及适应度自适应选择函数平衡ABC的勘探和开发能力;其次,以LSSVR的预测精度为目标函数,并将其转化为IABC的适应度函数,以此构建出基于IABC优化LSSVR的预测模型;最后,以基坑监测数据为例,将IABC优化的LSSVR模型、ABC优化的LSSVR模型以及基于PSO的组合模型进行预测对比分析。结果表明,IABC增加了种群的多样性,提高了收敛精度,基于IABC优化的LSSVR模型预测的变形趋势更符合实际,预测精度高于对比模型。

Research on Deformation Prediction Based on LSSVR Optimized by IABC

It is difficult to determine the penalty parameter and kernel function parameter of least square support vector regression(LSSVR). Additionally, artificial bee colony(ABC) is easy to fall into local optimum and its convergence speed is slow. So, we propose an improved artificial bee colony(IABC) to optimize the parameters of LSSVR and do research on deformation prediction. First, IABC generates positive and negative populations to increase the diversity of the initial group using the reverse learning strategy. After one iteration, information is exchanged between the optimal food sources of two populations to achieve optimal selection. Furthermore, we design an adaptive weight function and adaptive selection function to balance the exploration and development capacity of ABC. Second, we consider the predictive accuracy of LSSVR as the objective function, and transform it into the fitness function of IABC, thereby building a prediction model based on IABC optimization LSSVR. Then, taking the monitoring data of foundation pit as an example, we compare the prediction effect of the LSSVR model optimized by IABC, the LSSVR model optimized by ABC, and the combination model based on PSO. The results show that IABC increases the diversity of the population and improves the convergence accuracy. The prediction trend based on the IABC optimized LSSVR model is more practical and the prediction accuracy is higher than the contrast model.