融合协同进化人工鱼群算法和SVM的雾霾预测方法

doi:10.16451/j.cnki.issn1003-6059.201808005

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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.

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	左姣姣
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关键词 ：人工鱼群算法, 协同进化, 支持向量机(SVM), 雾霾预测

Abstract：

Aiming at the increasingly serious haze pollution, a haze prediction method combining co-evolution 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 co-evolution strategies among subpopulations. Then, the main parameters of SVM are optimized by co-evolution 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.

Key words： Artificial Fish Swarm Algorithm Co-evolution Support Vector Machine(SVM) Haze Prediction

收稿日期: 2018-03-02

ZTFLH:

TP 301.6

基金资助:

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

通讯作者: 倪志伟,博士,教授,主要研究方向为人工智能、机器学习、云计算.E-mail:zhwnelson@163.com.

作者简介: 左姣姣,硕士研究生,主要研究方向为机器学习、数据挖掘.E-mail:zuojiaojiao@mail.hfut.edu.cn. 朱旭辉,博士,讲师,主要研究方向为智能计算、机器学习.E-mail:zx572916@ohi.edu. 李敬明,博士,讲师,主要研究方向为智能计算、数据挖掘.E-mail:homer998@163.com. 伍章俊,博士,副教授,主要研究方向为机器学习、数据挖掘.E-mail:wuzhangjun@hfut.edu.cn.

引用本文:

左姣姣, 倪志伟, 朱旭辉, 李敬明, 伍章俊. 融合协同进化人工鱼群算法和SVM的雾霾预测方法[J]. 模式识别与人工智能, 2018, 31(8): 725-739. ZUO Jiaojiao, NI Zhiwei, ZHU Xuhui, LI Jingming, WU Zhangjun. Haze Prediction Method Combining Co-evolution Artificial Fish Swarm Algorithm and Support Vector Machine. , 2018, 31(8): 725-739.

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http://manu46.magtech.com.cn/Jweb_prai/CN/10.16451/j.cnki.issn1003-6059.201808005 或 http://manu46.magtech.com.cn/Jweb_prai/CN/Y2018/V31/I8/725

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