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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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

Received: 02 March 2018

ZTFLH:

TP 301.6

Fund:

Supported by National Natural Science Foundation of China(No.91546108,71490725,71521001), Natural Science Foundation of Anhui Province(No.1708085MG169), Humanities and Social Science Research Project of Anhui Provincial Education Department(JS2017AJRW0135)

Corresponding Authors: NI Zhiwei, Ph.D., professor. His research interests include artificial intelligence, machine learning and cloud computing.

About author:: ZUO Jiaojiao, master student. Her research interests include machine learning and data mining. ZHU Xuhui, Ph.D., lecturer. His resear-ch interests include intelligent computing and machine learning. LI Jingming, Ph.D., lecturer. His resear-ch interests include intelligent computing and data mining. WU Zhangjun, Ph.D., associate professor. His research interests include machine learning and data mining.

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Cite this article:

ZUO Jiaojiao,NI Zhiwei,ZHU Xuhui等. Haze Prediction Method Combining Co-evolution Artificial Fish Swarm Algorithm and Support Vector Machine[J]. , 2018, 31(8): 725-739.

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

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