摘要
Abstract
In this paper,a random forest optimization model based on the whale optimization algorithm is proposed to optimize the main factors affecting the uncertainty in each link of the simulation process with respect to the problem of optimizing the uncertainty factors in the three-level supply chain simulation model.A novel heuristic arithmetic Whale Optimization Algorithm(WOA)is used to optimize the number of decision trees in the random forest model as well as the minimum number of samples required on the leaf nodes of each decision tree to improve the accuracy of the model.In addition,a comprehensive comparison is performed using popular machine learning methods,including Categorical Regression Trees(CART)and Support Vector Machines(SVM).The experimental results show that the accuracy of optimizing uncertainty factors based on the model proposed in this paper is better than other models.In addition,the algorithm is more reliable in terms of problem solving scheme and quality.关键词
随机森林(RF)/鲸鱼算法(WOA)/不确定性/三级供应链仿真Key words
random forest(RF)/whale algorithm(WOA)/uncertainty/three-level supply chain simulation分类
信息技术与安全科学
