本文選題：多目標優(yōu)化 + NSGA-Ⅱ算法��； 參考：《華東理工大學》2015年碩士論文

【摘要】：基于流程仿真模型的復(fù)雜化工過程優(yōu)化,往往需要較長的優(yōu)化時間,效率低下,本文致力于構(gòu)造流程仿真模型的Kriging代理模型、提高Kriging代理模型的精度、研究基于Kriging代理模型的序列優(yōu)化策略。代理模型的計算量比精確模型小得多,在模型精度得到保證的前提下,采用代理模型可以大大減少優(yōu)化過程中的計算量,提高工程優(yōu)化設(shè)計的效率。本文的主要工作與創(chuàng)新點有： 首先,本文簡單地介紹了Kriging代理模型的基本組成項、優(yōu)點以及近期學者關(guān)于Kriging模型的研究概況；對Kriging代理模型的機理、回歸模型、相關(guān)模型等作了一一闡述；針對Kriging代理模型建模采樣的方法、Kriging代理模型精度檢驗的標準作了相關(guān)說明。 其次,本文介紹了一種提高Kriging代理模型精度的方法——序列迭代優(yōu)化,闡述了序列迭代優(yōu)化的基本流程。然后考慮到原始序列優(yōu)化方法EGO(efficient global optimization)的局限性和遺傳算法的優(yōu)越性,本文采用遺傳算法搜索基于某類加點準則狀況下模型更新所需要的迭代插值點,構(gòu)造了基于遺傳算法的序列優(yōu)化的操作流程；基于EI(Expected Improvement)加點準則的加權(quán)思想,本文提出了一種全新的加點準則——DH值最大點插值準則,并利用遺傳算法的全局搜索能力搜索模型迭代的插值點,提高了Kriging模型的建模精度。 另外,本文介紹了NSGA-Ⅱ算法的主要原理和基本流程,并將其成功地運用于對Kriging代理模型的操作優(yōu)化。DH值最大點插值法綜合了Kriging模型的預(yù)測誤差和均方差,本文選擇各子目標的DH值作為評價函數(shù)值,利用NSGA-Ⅱ算法,構(gòu)造了一種新的多目標序列優(yōu)化方法——MODH。 MODH算法利用NSGA-Ⅱ算法的遺傳操作和非支配排序等操作算子尋找基于Kriging模型的非支配解集,將這些非支配解集作為迭代點實現(xiàn)原始Kriging模型的更新,提高了Kriging代理模型的精度,最后選用了POL測試函數(shù)證明了上述多目標序列優(yōu)化方法的可行性。 最后,本文提出了一種Kriging代理模型與NSGA-Ⅱ算法兩者相結(jié)合的算法——K-N算法。K-N算法將基于Kriging代理模型優(yōu)化所得的Pareto解集作為新一輪NSGA-Ⅱ算法的初始種群,引導算法快速地在最優(yōu)解集附近尋優(yōu)。K-N算法綜合了Kriging代理模型效率高的特點和流程仿真模型準確度高的優(yōu)點,在PX氧化反應(yīng)過程的操作優(yōu)化中獲得了很好的應(yīng)用。
[Abstract]:Complex chemical process optimization based on process simulation model often needs long optimization time and low efficiency. In this paper, the Kriging agent model of process simulation model is constructed to improve the accuracy of Kriging agent model. The sequence optimization strategy based on Kriging proxy model is studied. The computational complexity of the agent model is much smaller than that of the accurate model. On the premise of ensuring the accuracy of the model, the calculation amount in the optimization process can be greatly reduced and the efficiency of the engineering optimization design can be improved by using the agent model. The main work and innovations of this paper are as follows: Firstly, this paper briefly introduces the basic components and advantages of Kriging proxy model, as well as the recent research situation of Kriging model, and expounds the mechanism of Kriging proxy model, regression model, relevant model and so on. In this paper, the Kriging proxy model sampling method and the accuracy test standard of Kriging agent model are introduced. Secondly, this paper introduces a method to improve the accuracy of Kriging proxy model-sequence iterative optimization, and describes the basic process of sequence iterative optimization. Then, considering the limitation of the original sequence optimization method EGO(efficient global optimization and the superiority of genetic algorithm, this paper uses genetic algorithm to search the iterative interpolation points needed for model updating based on certain additive criteria. The operation flow of sequence optimization based on genetic algorithm is constructed, and based on the weighted idea of EI(Expected improvement criterion, a new addition point criterion, DH maximum point interpolation criterion, is proposed in this paper. The global search ability of genetic algorithm is used to search the interpolation points of model iteration, which improves the modeling accuracy of Kriging model. In addition, this paper introduces the main principle and basic flow of NSGA- 鈪，

本文編號：1892515