本文選題：區(qū)間參數(shù)多目標(biāo)優(yōu)化　切入點(diǎn)：進(jìn)化算法　出處：《中國礦業(yè)大學(xué)》2017年碩士論文

【摘要】：區(qū)間參數(shù)多目標(biāo)優(yōu)化問題(Interval Multi-objective Optimization Problems,IMOPs)在實(shí)際生產(chǎn)、生活中十分常見,而且非常重要,但是由于其目標(biāo)變量具有不確定參數(shù),使得該類問題很難利用已有的多目標(biāo)進(jìn)化優(yōu)化算法(Multi-objective Optimization Evolutionary Algorithms,MOEAs)對其進(jìn)行求解。目前,區(qū)間參數(shù)多目標(biāo)優(yōu)化問題已經(jīng)是進(jìn)化優(yōu)化方向的研究重點(diǎn)之一,利用文化基因算法(Memetic Algorithms,MAs)是一種處理該類優(yōu)化問題的有效方法。此外,利用代理模型去簡化局部搜索過程,可以在保證結(jié)果精度的前提下,大大提高運(yùn)算效率�；诖饲疤�,本文給出一種基于代理模型的文化基因區(qū)間參數(shù)多目標(biāo)進(jìn)化優(yōu)化算法,下面將詳細(xì)介紹本文算法。首先,本課題在文化基因的算法架構(gòu)中,融入改進(jìn)的局部搜索策略,提出一種基于文化基因的區(qū)間參數(shù)多目標(biāo)進(jìn)化優(yōu)化算法(Interval Multi-objective Memetic Algorithm,IMOMA)。該算法主要包括全局搜索和局部搜索兩個部分,全局搜索采用基于區(qū)間占優(yōu)關(guān)系的IP-MOEA,局部搜索是本部分的研究重點(diǎn)。研究局部搜索環(huán)節(jié)時,主要有三個關(guān)鍵技術(shù):局部搜索激活機(jī)制,局部搜索初始種群的建立和局部搜索策略。通過10個區(qū)間意義下的基準(zhǔn)測試函數(shù)和太陽能海水淡化中不確定優(yōu)化問題的計算,并與不含局部搜索策略的IP-MOEA進(jìn)行比較,得到了收斂性、分布性良好且不確定度小的近似Pareto最優(yōu)解集,能夠說明所提算法IMOMA要優(yōu)于IP-MOEA。但是由于IMOMA多次使用超體積測度,導(dǎo)致算法時間復(fù)雜度過大,運(yùn)算效率低下。然后,針對IMOMA運(yùn)算效率低等問題,本文旨在局部搜索中融入代理模型,簡化復(fù)雜的適應(yīng)度評價,提出一種基于代理模型的文化基因區(qū)間多目標(biāo)進(jìn)化優(yōu)化算法(Surrogate Assisted Interval Multi-Objective Memetic Algorithm,SS-IMOMA)。算法架構(gòu)依然延續(xù)IMOMA的設(shè)計,兩者的區(qū)別主要體現(xiàn)是:在局部搜索策略中,基于個體的超體積貢獻(xiàn)與不確定度重新定義個體的適應(yīng)度函數(shù),并利用支持向量機(jī)(Support Vector Machine,SVM)去代理該單目標(biāo)適應(yīng)度評價,以達(dá)到提高運(yùn)行效率的目的。同樣的是,通過對10個區(qū)間意義下的基準(zhǔn)測試函數(shù)和太陽能海水淡化問題中的不確定優(yōu)化問題進(jìn)行優(yōu)化計算,SS-IMOMA比不含局部搜索的IP-MOEA擁有更好的算法性能,比不含代理模型的IMOMA擁有更小的時間復(fù)雜度。最后,利用MATLAB提供的GUI技術(shù)設(shè)計一個關(guān)于太陽能海水淡化問題的回歸及優(yōu)化平臺。平臺對原始數(shù)據(jù)的輸出進(jìn)行區(qū)間化處理,用來模擬實(shí)際工程中的不確定性問題;緊接著,本部分對具有區(qū)間參數(shù)的數(shù)據(jù)進(jìn)行支持向量機(jī)回歸分析,建立輸入與輸出之間的映射關(guān)系,以此克服實(shí)際工程中數(shù)值模型難以建立的問題。利用SS-IMOMA可以獲得太陽能能海水淡化問題的最優(yōu)解集,即該問題的最佳運(yùn)行工況。為了能夠方便地修改算法參數(shù),直觀地顯示運(yùn)行結(jié)果,該GUI平臺還提供了算法尋優(yōu)參數(shù),回歸曲線等圖像的顯示模塊,最優(yōu)解集等表格的輸出模塊,算法參數(shù)設(shè)置模塊,以及打開文件等控件的設(shè)計。綜上所述,本文所提的IMOMA能夠為區(qū)間參數(shù)多目標(biāo)優(yōu)化問題提供可靠有效的解決途徑。針對IMOMA運(yùn)行效率低的問題,本文所提的SS-IMOMA也成功的解決了此問題。特別是針對區(qū)間意義下的基準(zhǔn)測試函數(shù)與太陽能海水淡化中的不確定優(yōu)化問題,本文所提的IMOMA和SS-IMOMA都能夠得到較好的近似Pareto最優(yōu)解集。
[Abstract]:The multi-objective optimization problem of interval parameters (Interval Multi-objective Optimization Problems, IMOPs) in the actual production, life is very common, and very important, but because of the target variables with uncertain parameters, so the question is very difficult to use the existing evolutionary multi-objective optimization algorithm (Multi-objective Optimization Evolutionary Algorithms, MOEAs) to solve the problem. At present, the interval parameter multi-objective optimization problem has been one of the key research direction is evolutionary optimization, using genetic algorithm (Memetic Algorithms culture, MAs) is an effective method to deal with the problem. In addition, to simplify the local search process by the agent model, to ensure accuracy, greatly improve the efficiency of operation based on this premise, this paper presents a multi-objective evolutionary optimization algorithm based on cultural gene agent model with interval parameters, The following will detail the algorithm in this paper. Firstly, the algorithm architecture in the cultural gene of this subject, into the local search strategy improved, this paper proposes a multi-objective evolutionary optimization algorithm based on interval parameter gene (Interval Multi-objective Memetic culture Algorithm, IMOMA). The algorithm mainly includes two parts: global search and local search, global search the interval dominance relationship based on IP-MOEA, the local search is the focus of this study. Part of the study of local search links, there are three key points: the local search mechanism of activation of local search, the establishment of the initial population and local search strategies. Through the 10 interval under the benchmark function and uncertainty in solar desalination the calculation of the optimization problem, and compared with and without local search strategy IP-MOEA, convergence is, good distribution and uncertainty in small Like the Pareto optimal solution set, to show that the IMOMA algorithm is better than IP-MOEA. because the IMOMA repeatedly used the hypervolume measure, the algorithm of time complexity, the operation efficiency is low. Then, aiming at the problem of low efficiency of IMOMA algorithm, this paper aims to integrate the local search agent model, simplify the complexity of the fitness evaluation, put forward a multiobjective evolutionary optimization algorithm based on cultural gene region agent model (Surrogate Assisted Interval Multi-Objective Memetic Algorithm, SS-IMOMA). The algorithm is still a continuation of IMOMA architecture design, the main difference between the show is: in the local search strategy, hyper volume contribution of individual uncertainty and redefine the individual fitness function based on, and using support vector machine (Support Vector Machine, SVM) to represent the single objective fitness evaluation, in order to improve the efficiency of the same purpose. Is that through the benchmark functions and the solar desalination problem in the uncertain optimization optimization problem of 10 interval sense, SS-IMOMA has better performance than the IP-MOEA algorithm with local search, has much smaller than that without agent model the time complexity of IMOMA. Finally, the design of a solar seawater desalination problem regression and optimization platform provided by MATLAB GUI technology platform. The original data output interval, used to simulate the practical engineering problem of uncertainty; then, this part of the support vector machine regression analysis of interval parameter data, establishes the mapping relationship between input and output, in order to overcome the numerical model is difficult to establish the problem in practical engineering. The solar desalination can obtain optimal problem solution set can use SS-IMOMA, that is the problem of the operation Condition. In order to be able to easily modify the algorithm parameters, display the operation results, the GUI platform also provides the optimization parameters, such as image display module regression curve, optimal solution set form output module, parameter setting module, design and open the file control. To sum up, the IMOMA can provide reliable and effective solutions for the multi-objective optimization problem of interval parameters. For the low efficiency of IMOMA operation, the SS-IMOMA also successfully solved this problem. Especially for the interval under benchmark test functions and solar desalination of uncertain optimization problems, this paper proposed IMOMA and SS-IMOMA can get better approximate Pareto optimal set.

【學(xué)位授予單位】：中國礦業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：P747;TP18

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