基于IHSA_LSSVM的供应链绩效评估研究

为有效评估供应链绩效,结合和声搜索算法(IHSA)与最小二乘支持向量机,提出一种评估算法(IHS_LSSVM)。研究和声搜索算法的原理,对基音调整概率和基音调整步长进行动态调整,给出一种改进的和声搜索算法。利用该算法的全局搜索能力优化选取LSSVM的惩罚因子r和高斯核函数的半径σ。采用供应链绩效评估实例,构建供应链评估模型。仿真实验结果表明,与已有的BP神经网络和LSSVM等评估算法相比,IHS_LSSVM具有更小的预测误差和更高的预测精度。

Study of Supply Chain Performance Evaluation Based on IHSA_LSSVM

For supply chain performance evaluation, an algorithm is proposed based on the Improved Harmony Search Algorithm（IHSA） combined with the Least Square Support Vector Machine（LSSVM）. Studying the principle of Harmony Search Algorithm（HSA）, an improved harmony search algorithm uses dynamic adjustment of pitch adjusted probability and pitch adjustment step. The global search ability of the algorithm is used to select LSSVM penalty factor and Gaussian kernel function radius. Combined with a supply chain performance evaluation example, it builds supply chain assessment model. Simulation results show that with the existing BP neural network algorithm and LSSVM peer evaluation to quantify, it is shown that IHS_LSSVM has a smaller prediction error and higher prediction accuracy.