【摘要】：無論生產或生活過程中,人們總能遇到大量的復雜多目標優(yōu)化問題,此類問題一般具有多個自變量,多個等式或不等式約束條件以及多個非線性的目標量等。利用傳統(tǒng)的方法,如加權法,約束法等不能很好地解決此類問題,而多目標演化算法可以不受問題規(guī)則特性的限制,具有多種優(yōu)點,獲得了顯著的成果。多目標演化算法主要由新解產生和環(huán)境選擇兩部分組成,目前大部分關注與研究內容聚集在環(huán)境選擇方面,對新解產生算子鉆研與學習非常少。故本文將目前所盛行的機器學習中一種典型的方法——聚類技術與多目標演化算法合理融合,充分考慮問題的規(guī)則特性,為算法研究高效的,經改良的新解產生方式,使算法具有更佳的求解性能。首先,本文針對多目標分布估計算法對問題的規(guī)則特性考慮不夠,對群體演化過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,巨大的計算開銷用于構建最優(yōu)概率模型等不足,研究了一種基于聚類技術改進的多目標分布估計算法(CEDA)。CEDA在每一次循環(huán)迭代中利用凝聚層次聚類算法對種群數(shù)據進行分析,得出群體解分布結構信息,基于此結構信息,為所有解均建立一個多元高斯模型,依據此模型選擇適當?shù)臉颖?獲得新個體。為了降低建模計算開銷,鄰近個體共享相同的協(xié)方差矩陣建立高斯模型�；跇藴蕼y試題對比結果顯示CEDA可以解決十分復雜的問題。然后,本文針對多目標粒子群算法在求解過程時,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改進的MOPSO(CPSO)。CPSO在每一次迭代循環(huán)產生新解過程中,運用聚類算法對所有個體聚類分析,每一個個體的配對個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,自適應的調整新解產生方式為粒子群算法或多樣性保持好的復合差分進化算法�；跇藴蕼y試題對比實驗表明CPSO同樣能夠解決復雜的問題。最后,本文將新研究的兩種基于聚類的多目標演化算法應用于返回式衛(wèi)星艙布局優(yōu)化與某輕型飛機的齒輪減速器優(yōu)化設計問題中,求證了新算法在解決實際工程應用中表現(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.
【學位授予單位】：哈爾濱工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP18

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