基于自适应精英蚁群算法的GM（1,1）预测模型

在GM(1,1)预测模型中，发展系数a和灰色作用量b两参数对模型的预测精度有直接影响。在分析GM建模原理和参数对模型精度影响的基础上，提出了一种信息素浓度自适应调整的精英ACO算法与GM（1,1）融合预测模型，在不改变GM(1,1)模型表达形式前提下，使用了改进的ACO算法来求解模型的最优参数。试验结果表明：与传统的GM(1,1)模型相比，改进的ACO算法与GM（1,1）融合模型的预测精度在传统GM模型误差较大的情况下也能得到较好的预测效果，在适用性上比传统模型具有优越性，是提升模型精度一种新思路。同时也说明了运用自适应精英策略改进蚁群算法提升算法全局寻优能力是合理的科学的。

A GM (1,1) Prediction Model based on Adaptive Elite Ant Colony Algorithm

In the GM (1,1) prediction model, the development coefficient a and grey action b have a direct impact on the prediction accuracy of the model. Based on the analysis of GM modeling principle and the influence of parameters on model accuracy, an elite ACO algorithm with adaptive pheromone concentration adjustment and GM (1,1) fusion prediction model are proposed. Without changing the expression of GM (1,1) model, the improved ACO algorithm is used to solve the optimal parameters of the model. The experimental results show that compared with the traditional GM (1,1) model, the prediction accuracy of the improved ACO algorithm and GM (1,1) fusion model can also get better prediction effect under the condition of large error of the traditional GM model, and has advantages over the traditional model in applicability. It is a new idea to improve the accuracy of the model. It also shows that it is reasonable and scientific to use the adaptive elite strategy to improve the ant colony algorithm and improve the global optimization ability of the algorithm.