融合协同进化人工鱼群算法和SVM的雾霾预测方法  

Haze Prediction Method Combining Co-evolution Artificial Fish Swarm Algorithm and Support Vector Machine

作　　者：左姣姣 倪志伟[1,2] 朱旭辉[1,2] 李敬明 伍章俊[1,2] ZUO Jiaojiao;NI Zhiwei;ZHU Xuhui;LI Jingming;WU Zhangjun(School of Management,Hefei University of Technology,Hefei 230009;Key Laboratory of Process Optimization and Intelligent Decision-Making of Ministry of Education,Hefei University of Technology,Hefei 230009;School of Management Science and Engineering,Anhui University of Finance and Economics,Bengbu 233030)

机构地区：[1]合肥工业大学管理学院,合肥230009 [2]合肥工业大学过程优化与智能决策教育部重点实验室,合肥230009 [3]安徽财经大学管理科学与工程学院,蚌埠233030

出　　处：《模式识别与人工智能》2018年第8期725-739,共15页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金项目(No.91546108;71490725;71521001);安徽省自然科学基金项目(No.1708085MG169);安徽省教育厅人文社会科学研究项目(JS2017AJRW0135)资助~~

摘　　要：针对日益严重的雾霾污染问题,提出融合协同进化人工鱼群算法和支持向量机的雾霾预测方法.首先,运用佳点集构造均匀分布的种群,并引入自适应视野范围策略、自适应步长策略、种群间协同策略,提出协同进化人工鱼群算法.然后,使用协同进化人工鱼群算法,优化支持向量机的主要参数.最后,构建基于支持向量机的雾霾预测模型,预测雾霾天气.在10个测试函数上的实验证明协同进化人工鱼群算法的性能,在6个UCI数据集上的实验验证预测模型的稳定性和有效性.Aiming at the increasingly serious haze pollution, a haze prediction method combining coevolution artificial fish swarm algorithm （CEAFSA） and support vector machine （SVM） is proposed. Firstly, an improved artificial fish swarm algorithm is proposed by initializing evenly distributed population using the good point set, and introducing adaptive strategies for visual scope and step and coevolution strategies among subpopulations. Then, the main parameters of SVM are optimized by coevolution artificial fish swarm algorithm. Finally, haze prediction model is established by SVM. Experimental results on 10 Benchmark testing functions verify the validity of CEAFSA and the results on 6 UCI datasets demonstrate its high stability and effectiveness.

关 键 词：人工鱼群算法 协同进化 支持向量机(SVM) 雾霾预测 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]