本文選題：人工魚群算法　切入點：協(xié)同進化　出處：《系統(tǒng)工程理論與實踐》2017年04期 　論文類型：期刊論文

【摘要】：鑒于目前日益嚴重的霧霾污染,導致空氣質(zhì)量水平大幅下降,通過采用協(xié)同進化離散型人工魚群算法,多重分形維數(shù),并結(jié)合極限學習機,提出了融合協(xié)同進化離散型人工魚群算法和多重分形的霧霾預測方法.首先使用佳點集理論初始化種群,通過引入人工魚游速,改進人工魚群算法聚群,追尾和覓食行為,及對其進行離散化,并引入競爭和合作機制;其次將協(xié)同進化離散型人工魚群算法結(jié)合多重分形維數(shù),對霧霾數(shù)據(jù)集進行約簡;最后運用極限學習機建立霧霾預測模型.通過對北京,上海和廣州三地區(qū)近兩年的霧霾數(shù)據(jù)集進行實驗及參數(shù)分析,實驗結(jié)果表明,較其他方法,預測性能更優(yōu),具有良好的穩(wěn)定性和可信性.
[Abstract]:In view of the serious pollution of haze at present, the level of air quality is greatly reduced. By using co-evolution discrete artificial fish swarm algorithm, multifractal dimension and extreme learning machine are used. In this paper, a haze prediction method based on co-evolution discrete artificial fish swarm algorithm and multifractal algorithm is proposed. Firstly, the population is initialized by using the theory of good point set. By introducing artificial fish swimming speed, the artificial fish swarm algorithm is improved, and the behavior of the artificial fish swarm clustering, rear-end and foraging is improved. Secondly, the co-evolution discrete artificial fish swarm algorithm is combined with multifractal dimension to reduce the haze data set. Finally, the haze prediction model is established by using the extreme learning machine. Through the experiment and parameter analysis of the haze data sets in Beijing, Shanghai and Guangzhou in the past two years, the experimental results show that the prediction performance is better than other methods. With good stability and credibility.
【作者單位】： 合肥工業(yè)大學管理學院;過程優(yōu)化與智能決策教育部重點實驗室;美國俄亥俄大學工程學院工業(yè)與系統(tǒng)工程系;
【基金】：國家自然科學基金重大研究計劃培育項目(91546108);國家自然科學基金(71271071);國家自然科學基金重大項目(71490725);國家自然科學基金青年項目(71301041)~~
【分類號】：TP18;X513

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1 武鎖慶;人地協(xié)同進化與區(qū)域可持續(xù)發(fā)展[D];成都理工學院;2001年

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本文編號：1590641