基于改进万有引力优化的LSSVM模型在标签缺陷检测中的应用

针对最小二乘支持向量机(LSSVM)在缺陷检测过程中的模型参数选择问题,提出了一种改进的万有引力搜索算法(IGSA)对模型参数进行优化,该算法有效地克服了标准GSA易陷入局部最优解且优化精度不高的缺点,显著提高了原算法中物体的探索能力与开发能力。通过利用UCI数据库的数据进行分类验证,相比交叉验证、标准GSA、遗传和粒子群优化的LSSVM,IGSA-LSSVM分类模型有效提高了分类正确率和模型的泛化能力。最后,把该模型应用于标签缺陷自动检测中,取得了良好的效果。

LSSVM model optimized by improved Gravitation Search Algorithm and its application on label defectsSdetectingS

In the light of the problems existed in selecting the parameters of LSSVM model in the process of defect detection, The Improved Gravitational Search Algorithm (IGSA) is brought in and applied to optimize the model parameters of LSSVM. The algorithm overcomes the shortcoming of standard GSA that is easy to fall into local optimum and has low accuracy and effectively improves the exploration ability and development ability of GSA. Experiments are carried out on the data sets from the UCI database, Compared with cross-validation, standard GSA, Genetic Algorithm and Particle Swarm Optimization, the IGSA has the better classification accuracy and generalization ability. Finally,this model is applied to the label defect detection with a good result.