本文選題：多目標(biāo)優(yōu)化　切入點(diǎn)：多目標(biāo)進(jìn)化算法　出處：《華南理工大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

【摘要】：近年來,隨著移動(dòng)互聯(lián)網(wǎng)、云計(jì)算等技術(shù)的發(fā)展,互聯(lián)網(wǎng)金融理財(cái)越來越受到人們的關(guān)注,各種基金理財(cái)產(chǎn)品可以很方便地通過網(wǎng)上平臺(tái)、移動(dòng)app應(yīng)用購買。目前市場存在著多種基金理財(cái)產(chǎn)品,對于個(gè)人投資者以及互聯(lián)網(wǎng)金融理財(cái)平臺(tái),如何在眾多的基金產(chǎn)品中選擇最優(yōu)的投資組合,具有十分重要意義�；鹄碡�(cái)本質(zhì)上是一種投資組合問題,追求風(fēng)險(xiǎn)和收益兩個(gè)重要目標(biāo),是典型的多目標(biāo)優(yōu)化問題,實(shí)際問題中基金種類較多,有很多附加限制例如交易費(fèi)用、投資種類等,計(jì)算量龐大。多目標(biāo)進(jìn)化算法是一類可以有效求解復(fù)雜組合優(yōu)化問題的算法,被廣泛地應(yīng)用于投資組合等實(shí)際問題。本文基于經(jīng)典的投資組合均值方差模型,提出了新的改進(jìn)模型,并根據(jù)實(shí)際問題,對NSGA-II算法的初始數(shù)據(jù)處理方式、遺傳交叉和變異算子進(jìn)行了改進(jìn)。實(shí)驗(yàn)表明,這些改進(jìn)顯著地提高了算法的計(jì)算效率,并改善了應(yīng)用效果。本文主要工作和創(chuàng)新點(diǎn)如下:1.以現(xiàn)代投資組合理論為基礎(chǔ),在經(jīng)典的單目標(biāo)均值方差模型上,考慮了交易費(fèi)用、投資種類、購買價(jià)格水平等因素,提出了改進(jìn)的多目標(biāo)優(yōu)化模型,該模型適合解決實(shí)際問題;2.重點(diǎn)查閱和研究了典型的多目標(biāo)進(jìn)化算法——NSGA-II,實(shí)現(xiàn)了該算法對模型的求解,同時(shí)通過實(shí)驗(yàn)比較了它與其它進(jìn)化算法的收斂性和時(shí)間性能;3.針對NSGA-II算法在求解基金投資組合實(shí)際問題時(shí)時(shí)間過長、收斂性不夠理想的問題,提出了有效的改進(jìn)方法,首先改進(jìn)初始數(shù)據(jù)的預(yù)處理方式,預(yù)先進(jìn)行非支配選擇,縮小了問題決策空間;然后采用二進(jìn)制編碼方式代替標(biāo)準(zhǔn)算法的實(shí)數(shù)編碼方式,提出改進(jìn)的交叉變異算子,定義了輔助可行解修復(fù)操作,滿足改進(jìn)模型的約束條件,降低了模型的求解復(fù)雜度。這些改進(jìn)顯著地提升了計(jì)算效率,改善了應(yīng)用效果。4.為驗(yàn)證算法的應(yīng)用效果,本文采用了兩個(gè)數(shù)據(jù)集,一個(gè)數(shù)據(jù)集是標(biāo)準(zhǔn)數(shù)據(jù)集,用于驗(yàn)證多目標(biāo)進(jìn)化算法對求解投資組合問題的有效性;另一個(gè)數(shù)據(jù)集是網(wǎng)上爬取的實(shí)際基金數(shù)據(jù),并進(jìn)行了對比實(shí)驗(yàn)與結(jié)果分析,充分驗(yàn)證模型和算法改進(jìn)的有效性。本文詳細(xì)研究了基金投資組合問題,提出了實(shí)際有效的改進(jìn)多目標(biāo)優(yōu)化模型;通過編程爬取國內(nèi)真實(shí)的基金數(shù)據(jù)信息,最終實(shí)現(xiàn)了多目標(biāo)進(jìn)化算法對模型的優(yōu)化求解。此外還針對標(biāo)準(zhǔn)算法計(jì)算效率與應(yīng)用效果較差的缺點(diǎn),提出了有效的改進(jìn)方法。因此,本文的研究工作對于拓展多目標(biāo)進(jìn)化算法的應(yīng)用、對于個(gè)人投資者和基金理財(cái)平臺(tái)選取最佳投資組合有一定的理論和實(shí)際意義。
[Abstract]:In recent years, with the development of mobile Internet, cloud computing and other technologies, people pay more and more attention to Internet financial management. At present, there are many kinds of fund management products in the market. For individual investors and Internet financial management platform, how to choose the best portfolio among the many fund products, Fund financing is essentially a kind of portfolio problem, which pursues two important objectives of risk and income, and is a typical multi-objective optimization problem. In the practical problems, there are many kinds of funds. There are many additional restrictions, such as transaction costs, types of investment, and so on, and the computation is huge. Multi-objective evolutionary algorithm is a kind of algorithm that can effectively solve complex combinatorial optimization problems. Based on the classical portfolio mean variance model, a new improved model is proposed. According to the actual problem, the initial data processing method of NSGA-II algorithm is discussed. The genetic crossover and mutation operators have been improved. Experiments show that these improvements have significantly improved the computational efficiency of the algorithm and improved the effect of application. The main work and innovations of this paper are as follows: 1. Based on the modern portfolio theory, Based on the classical single-objective mean variance model, an improved multi-objective optimization model is proposed, which takes into account the transaction cost, investment type, purchase price level and so on. The model is suitable for solving practical problems. The typical multi-objective evolutionary algorithm NSGA-IIs is studied, and the model is solved by this algorithm. At the same time, the convergence and time performance of the algorithm and other evolutionary algorithms are compared by experiments. Aiming at the problem that the NSGA-II algorithm takes too long to solve the actual problem of fund portfolio, and the convergence is not ideal, an effective improvement method is proposed. Firstly, the preprocessing method of the initial data is improved, and the non-dominant selection is carried out in advance, which reduces the decision space of the problem, and then the binary coding method is used to replace the real number coding method of the standard algorithm, and an improved crossover mutation operator is proposed. The auxiliary feasible solution repair operation is defined, the constraint condition of the improved model is satisfied, and the complexity of solving the model is reduced. These improvements have significantly improved the computational efficiency and improved the application effect .4.In order to verify the application effect of the algorithm, In this paper, two data sets are used, one is the standard data set, which is used to verify the effectiveness of the multi-objective evolutionary algorithm for solving portfolio problems, the other is the actual fund data crawling on the net. The comparison experiment and result analysis are carried out to verify the effectiveness of the improved model and algorithm. In this paper, the fund portfolio problem is studied in detail, and a practical and effective improved multi-objective optimization model is proposed. Finally, the optimization solution of the model by multi-objective evolutionary algorithm is realized by crawling the real fund data information in China. In addition, an effective improvement method is proposed to solve the problem of poor computational efficiency and application effect of the standard algorithm. The research work in this paper is of theoretical and practical significance to expand the application of multi-objective evolutionary algorithm and to select the best portfolio for individual investors and fund management platforms.
【學(xué)位授予單位】：華南理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：F724.6;F832.2;TP301.6

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