本文選題：算法 + 甲醇合成��； 參考：《化工學報》2017年08期

【摘要】：為了提高精英教學算法(ETLBO)的尋優(yōu)能力,特別是精度差、尋優(yōu)速度慢的問題,提出改進的精英教學算法。首先,通過自主學習過程,加強對優(yōu)質(zhì)解所在區(qū)域的局部勘探,提高算法的尋優(yōu)效率。其次,引入"差異化幫扶"思想及自適應機制,對不同水平的學生施予適宜的、靈活的學習方式,有針對性的幫助,平衡了算法的勘探速度、精度。通過增加學生間的交流次數(shù),提高了算法的全局勘探能力。標準函數(shù)優(yōu)化結果表明,改進后的算法在尋優(yōu)能力和勘探效率兩方面都有明顯提高。最后,建立甲醇合成的機理模型,將改進后的算法應用于甲醇合成過程的優(yōu)化,取得了良好的效果。
[Abstract]:In order to improve the optimization ability of elite teaching algorithm, especially the problems of poor precision and slow optimization speed, an improved elite teaching algorithm is proposed. Firstly, through the autonomous learning process, the local exploration of the high quality solution area is strengthened, and the optimization efficiency of the algorithm is improved. Secondly, the idea of "differential help" and the adaptive mechanism are introduced to give appropriate and flexible learning methods to students at different levels and to help them, thus balancing the exploration speed and precision of the algorithm. By increasing the number of communication between students, the global exploration ability of the algorithm is improved. The results of standard function optimization show that the improved algorithm has significant improvement in both optimization ability and exploration efficiency. Finally, the mechanism model of methanol synthesis is established, and the improved algorithm is applied to the optimization of methanol synthesis process, and good results are obtained.
【作者單位】： 華東理工大學化工過程先進控制與優(yōu)化技術教育部重點實驗室;
【基金】：國家自然科學基金項目(61573144)~~
【分類號】：TP18;TQ223.121

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相關期刊論文 前3條

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本文編號：1866163