發(fā)布時(shí)間：2018-03-23 07:04

  本文選題：蛙跳算法　切入點(diǎn)：支持向量回歸機(jī)　出處：《新疆大學(xué)》2017年碩士論文　論文類(lèi)型：學(xué)位論文

【摘要】：蛙跳算法(Shuffled Frog Leaping Algorithm,SFLA)自提出以來(lái),引起學(xué)者的廣泛關(guān)注,并在部分工程領(lǐng)域得到了成功的應(yīng)用。SFLA算法在解決高維問(wèn)題時(shí)收斂速度較為緩慢且容易陷入局部最優(yōu),為了改善SFLA算法的搜索性能,本文通過(guò)對(duì)其內(nèi)部尋優(yōu)結(jié)構(gòu)分析,提出了幾種改進(jìn)的SFLA算法,并應(yīng)用于氧化還原電位(Oxidation Reduction Potential,ORP)預(yù)測(cè)以及作業(yè)車(chē)間調(diào)度優(yōu)化問(wèn)題中。本文的主要研究工作如下:1)為了提高SFLA在求解高維問(wèn)題時(shí)的收斂速度以及避免陷入局部最優(yōu),提出了基于局部尋優(yōu)策略改進(jìn)的ISFLA(Improved Shuffled Frog Leaping Algorithm)和與教學(xué)算法(Teaching-Learning-Based Optimization Algorithm)相結(jié)合的TLBO-SFLA兩種改進(jìn)算法。在ISFLA算法的尋優(yōu)過(guò)程中,分別引入混沌序列對(duì)種群初始化,擴(kuò)大了初始種群的搜索范圍;引入粒子群的局部更新策略,加強(qiáng)了種群內(nèi)部的信息交流;引入反向?qū)W習(xí),增加了算法搜索后期解的多樣性,降低了陷入局部最優(yōu)的概率。在TLBO-SFLA算法中,則是將每個(gè)子種群看作一個(gè)班級(jí)來(lái)進(jìn)行學(xué)習(xí),最優(yōu)個(gè)體“老師”通過(guò)“教”的方式提升班級(jí)的整體水平,學(xué)生之間的相互學(xué)習(xí)過(guò)程可以更好的實(shí)現(xiàn)差異化學(xué)習(xí)。選取測(cè)試函數(shù)對(duì)所提兩種改進(jìn)算法進(jìn)行測(cè)試,實(shí)驗(yàn)結(jié)果表明所提改進(jìn)算法收斂速度更快尋優(yōu)精度更高。2)ORP作為細(xì)菌活性的重要評(píng)價(jià)指標(biāo),對(duì)ORP的精準(zhǔn)預(yù)測(cè)有利于實(shí)現(xiàn)對(duì)氧化提金過(guò)程關(guān)鍵參數(shù)的及時(shí)調(diào)控。為了實(shí)現(xiàn)ORP的預(yù)測(cè),建立了支持向量回歸機(jī)模型對(duì)ORP進(jìn)行預(yù)測(cè),并選用改進(jìn)的蛙跳算法對(duì)預(yù)測(cè)模型的關(guān)鍵參數(shù)進(jìn)行優(yōu)化,以達(dá)到較高的預(yù)測(cè)精度。選取新疆某金礦的實(shí)際生產(chǎn)數(shù)據(jù)建立預(yù)測(cè)模型,結(jié)果表明,基于改進(jìn)蛙跳算法優(yōu)化的支持向量回歸機(jī)ORP預(yù)測(cè)精度更高。3)提出了可用于求解作業(yè)車(chē)間調(diào)度優(yōu)化的基于工序編碼的OSFLA(Optimized Shuffled Frog Leaping Algorithm)算法,選取標(biāo)準(zhǔn)的調(diào)度問(wèn)題進(jìn)行仿真測(cè)試,實(shí)驗(yàn)結(jié)果表明,OSFLA算法不僅可以求出最優(yōu)解,而且搜索速度更快。
[Abstract]:Since it was put forward, the leapfrog Frog Leaping algorithm has attracted wide attention of scholars, and it has been successfully applied in some engineering fields. The convergence rate of .SFLA algorithm is slow and it is easy to fall into local optimum when solving the problem of high dimension. In order to improve the search performance of SFLA algorithm, this paper proposes several improved SFLA algorithms by analyzing its internal optimization structure. And it is applied to the oxidation-reduction potential Reduction potential orp prediction and job shop scheduling optimization problem. The main work of this paper is as follows: 1) in order to improve the convergence speed of SFLA in solving high dimensional problems and avoid falling into local optimum. Two improved algorithms, ISFLA(Improved Shuffled Frog Leaping algorithm based on local optimization strategy and TLBO-SFLA algorithm combined with Teaching-Learning-Based Optimization algorithm, are proposed. In the process of ISFLA optimization, chaotic sequences are introduced to initialize the population. The search range of initial population is enlarged, the local updating strategy of particle swarm is introduced, and the information exchange within population is strengthened. The diversity of the late solution of search algorithm is increased by introducing reverse learning. The probability of falling into local optimum is reduced. In the TLBO-SFLA algorithm, each subgroup is treated as a class to learn, and the optimal individual "teacher" improves the overall level of the class by "teaching". The process of mutual learning between students can better achieve differential learning. Select the test function to test the proposed two improved algorithms. The experimental results show that the improved algorithm has faster convergence speed and higher precision. 2ORP is an important evaluation index of bacterial activity. The accurate prediction of ORP is helpful to realize the timely control of the key parameters in the process of oxidizing gold extraction. In order to realize the prediction of ORP, The support vector regression model is established to predict ORP, and the improved leapfrog algorithm is used to optimize the key parameters of the prediction model to achieve a higher prediction accuracy. The actual production data of a gold mine in Xinjiang are selected to establish the prediction model. The results show that the ORP prediction accuracy of SVM based on improved leapfrog algorithm is higher. 3) A OSFLA(Optimized Shuffled Frog Leaping algorithm which can be used to solve job shop scheduling optimization is proposed. The standard scheduling problem is selected for simulation test. The experimental results show that the OSFLA algorithm can not only find the optimal solution, but also search faster.
【學(xué)位授予單位】：新疆大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TP18

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 王洋;劉志珍;;基于蛙跳模糊算法的Jiles Atherton鐵心磁滯模型參數(shù)確定[J];電工技術(shù)學(xué)報(bào);2017年04期

2 王玉昆;黨金梗;;基于KPCA與ILSFA-LSSVM的浮選加藥控制模型[J];控制工程;2017年02期

3 楊靜宗;王曉東;吳建德;;基于改進(jìn)SFLA-LSSVM的礦漿管道臨界淤積流速預(yù)測(cè)[J];云南大學(xué)學(xué)報(bào)(自然科學(xué)版);2017年01期

4 張曉琳;張沖;楊濤;;基于改進(jìn)布谷鳥(niǎo)算法的火焰圖像閾值分割算法[J];微電子學(xué)與計(jì)算機(jī);2017年01期

5 南新元;曾慶凱;孟憲強(qiáng);;基于改進(jìn)蛙跳算法優(yōu)化的支持向量回歸機(jī)ORP預(yù)測(cè)[J];新疆大學(xué)學(xué)報(bào)(自然科學(xué)版);2016年04期

6 常小剛;趙紅星;;基于三角函數(shù)搜索因子的混合蛙跳算法[J];計(jì)算機(jī)工程與科學(xué);2016年11期

7 李娜;南新元;劉燁;;基于改進(jìn)教學(xué)算法優(yōu)化WNN的ORP預(yù)測(cè)[J];計(jì)算機(jī)工程與設(shè)計(jì);2016年08期

8 肖瑩瑩;林廷宇;李伯虎;侯寶存;施國(guó)強(qiáng);;混合蛙跳算法自適應(yīng)參數(shù)調(diào)整改進(jìn)策略[J];系統(tǒng)工程與電子技術(shù);2016年08期

9 劉宏;王其濤;夏未君;;優(yōu)化混合蛙跳算法的WSN三維定位方法[J];計(jì)算機(jī)工程與應(yīng)用;2017年02期

10 張強(qiáng);李盼池;;自適應(yīng)分組混沌云模型蛙跳算法求解連續(xù)空間優(yōu)化問(wèn)題[J];控制與決策;2015年05期

相關(guān)博士學(xué)位論文 前4條

1 趙轉(zhuǎn)哲;混合蛙跳算法的改進(jìn)及在旋轉(zhuǎn)機(jī)械故障診斷中的應(yīng)用研究[D];東南大學(xué);2016年

2 程軍;基于生物行為機(jī)制的粒子群算法改進(jìn)及應(yīng)用[D];華南理工大學(xué);2014年

3 李騫;含砷金礦生物預(yù)氧化提金基礎(chǔ)研究[D];中南大學(xué);2007年

4 楊松榮;含砷難處理金礦石生物氧化提金基礎(chǔ)與工程化研究[D];中南大學(xué);2004年

相關(guān)碩士學(xué)位論文 前3條

1 李漢琛;基于支持向量機(jī)理論的股指期貨量化交易策略研究[D];浙江大學(xué);2016年

2 李偉;基于數(shù)據(jù)驅(qū)動(dòng)的氧化還原電位軟測(cè)量技術(shù)研究[D];新疆大學(xué);2015年

3 苑洪晶;協(xié)同強(qiáng)化含砷金礦生物預(yù)氧化的研究[D];中南大學(xué);2013年

，

本文編號(hào)：1652392