【摘要】：無(wú)論生產(chǎn)或生活過(guò)程中,人們總能遇到大量的復(fù)雜多目標(biāo)優(yōu)化問(wèn)題,此類問(wèn)題一般具有多個(gè)自變量,多個(gè)等式或不等式約束條件以及多個(gè)非線性的目標(biāo)量等。利用傳統(tǒng)的方法,如加權(quán)法,約束法等不能很好地解決此類問(wèn)題,而多目標(biāo)演化算法可以不受問(wèn)題規(guī)則特性的限制,具有多種優(yōu)點(diǎn),獲得了顯著的成果。多目標(biāo)演化算法主要由新解產(chǎn)生和環(huán)境選擇兩部分組成,目前大部分關(guān)注與研究?jī)?nèi)容聚集在環(huán)境選擇方面,對(duì)新解產(chǎn)生算子鉆研與學(xué)習(xí)非常少。故本文將目前所盛行的機(jī)器學(xué)習(xí)中一種典型的方法——聚類技術(shù)與多目標(biāo)演化算法合理融合,充分考慮問(wèn)題的規(guī)則特性,為算法研究高效的,經(jīng)改良的新解產(chǎn)生方式,使算法具有更佳的求解性能。首先,本文針對(duì)多目標(biāo)分布估計(jì)算法對(duì)問(wèn)題的規(guī)則特性考慮不夠,對(duì)群體演化過(guò)程中得到的異常解的處置方法欠佳,群體中解的多樣性容易丟失,巨大的計(jì)算開(kāi)銷用于構(gòu)建最優(yōu)概率模型等不足,研究了一種基于聚類技術(shù)改進(jìn)的多目標(biāo)分布估計(jì)算法(CEDA)。CEDA在每一次循環(huán)迭代中利用凝聚層次聚類算法對(duì)種群數(shù)據(jù)進(jìn)行分析,得出群體解分布結(jié)構(gòu)信息,基于此結(jié)構(gòu)信息,為所有解均建立一個(gè)多元高斯模型,依據(jù)此模型選擇適當(dāng)?shù)臉颖?獲得新個(gè)體。為了降低建模計(jì)算開(kāi)銷,鄰近個(gè)體共享相同的協(xié)方差矩陣建立高斯模型�；跇�(biāo)準(zhǔn)測(cè)試題對(duì)比結(jié)果顯示CEDA可以解決十分復(fù)雜的問(wèn)題。然后,本文針對(duì)多目標(biāo)粒子群算法在求解過(guò)程時(shí),雖然具有很高的收斂速度,但是容易丟失種群多樣性的不足,研究了基于聚類技術(shù)改進(jìn)的MOPSO(CPSO)。CPSO在每一次迭代循環(huán)產(chǎn)生新解過(guò)程中,運(yùn)用聚類算法對(duì)所有個(gè)體聚類分析,每一個(gè)個(gè)體的配對(duì)個(gè)體分別以確定的幾率從全局或局部種群挑選,另外為了更好的維持種群解的多樣性與算法的收斂速度之間的平衡,自適應(yīng)的調(diào)整新解產(chǎn)生方式為粒子群算法或多樣性保持好的復(fù)合差分進(jìn)化算法。基于標(biāo)準(zhǔn)測(cè)試題對(duì)比實(shí)驗(yàn)表明CPSO同樣能夠解決復(fù)雜的問(wèn)題。最后,本文將新研究的兩種基于聚類的多目標(biāo)演化算法應(yīng)用于返回式衛(wèi)星艙布局優(yōu)化與某輕型飛機(jī)的齒輪減速器優(yōu)化設(shè)計(jì)問(wèn)題中,求證了新算法在解決實(shí)際工程應(yīng)用中表現(xiàn)。
[Abstract]:Whether in production or life, people can always encounter a large number of complex multi-objective optimization problems, such problems generally have multiple independent variables, multiple equality or inequality constraints, as well as a number of nonlinear objective quantities and so on. The traditional methods, such as weighted method and constraint method, can not solve this kind of problem well, but the multi-objective evolutionary algorithm can not be restricted by the characteristic of the rule of the problem, so it has many advantages, and has obtained remarkable results. The multi-objective evolutionary algorithm is mainly composed of two parts: new solution generation and environment selection. At present, most of the research contents focus on the environment selection, and the research on the new solution generation operator is very little. In this paper, a typical method of machine learning, clustering technique and multi-objective evolutionary algorithm, is combined reasonably, and the rule characteristic of the problem is fully taken into account in this paper, which is an efficient and improved new solution generation method. The algorithm has better solution performance. First of all, the algorithm of multi-objective distribution estimation is not enough to consider the rule of the problem, and the method to deal with the abnormal solutions in the process of population evolution is poor, and the diversity of solutions in the population is easy to be lost. The huge computational overhead is used to build the optimal probability model and so on. In this paper, an improved multi-objective distribution estimation algorithm based on clustering technique, (CEDA). CEDA, is proposed to analyze the population data in each cycle iteration, and to obtain the distribution structure information of the population solution, based on the structure information. A multivariate Gao Si model is established for all solutions, according to which suitable samples are selected and new individuals are obtained. In order to reduce the overhead of modeling, neighboring individuals share the same covariance matrix to build Gao Si model. The comparison results based on standard test questions show that CEDA can solve very complex problems. Then, in order to solve the problem of multi-objective particle swarm optimization, although it has a high convergence rate, it is easy to lose the deficiency of population diversity. In this paper, we study the application of clustering algorithm to the clustering analysis of all individuals in the process of generating new solutions in each iteration cycle of MOPSO (CPSO). CPSO based on the improved clustering technology. In order to maintain the balance between the diversity of the population solution and the convergence rate of the algorithm, each individual is selected from the global or local population with a definite probability. Adaptive new solutions are generated by particle swarm optimization (PSO) or composite differential evolution (DEA) with good diversity. The contrast experiment based on standard test shows that CPSO can solve complex problems as well. Finally, in this paper, two new multi-objective evolutionary algorithms based on clustering are applied to the optimization of recoverable satellite cabin layout and the optimal design of gear reducer of a light aircraft, and the performance of the new algorithm in solving practical engineering applications is verified.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP18

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