基于蚁群算法求解Choquet模糊积分模型

为了提高Choquet模糊积分模糊测度的搜索效率,提出改进的蚁群算法求解模型。根据特征数量构建Choquet模糊积分模型,搜索过程中对每只蚂蚁按状态转移概率进行全局搜索或局部搜索,迭代搜索最优解,并由Fisher判别进行分类。试验使用3组癌症基因数据集,利用R语言的Bioconductor工具箱进行数据预处理,并分析对比新模型和主流算法的分类效果。结果表明:在DLBCL数据集和Colon数据集中,基于蚁群算法的Choquet模糊积分得到最好的分类效果;在Prostate数据集中,虽然和基于遗传算法的Choquet模糊积分分类效果接近,但是蚁群算法仍然很快收敛,改进的蚁群算法可以作为求解模糊测度的快速方法。

Method for solving Choquet integral model based on ant colony algorithm

An improved ant colony algorithm for Choquet integral was investigated to enhance the search efficiency of fuzzy measure. Choquet integral model was built according to the characteristic quantity and solved by the process of searching globally or locally according to the state transition probability. It was classified by Fisher discriminates. The experiment used three sets of cancer gene datasets preprocessed by R language Bioconductor toolkit, and classification results was analyzed between new model and the mainstream algorithm. The results showed that in DLBCL dataset and colon dataset, ant colony algorithm had the better effect; in prostate dataset, although the classification results were about the same, ant colony algorithm still had faster convergence than genetic algorithm. The improved ant colony algorithm presented a feasible and effective way to solve fuzzy measures in Choquet integral model.