發(fā)布時間：2018-03-02 14:00

  本文選題：工程優(yōu)化　切入點：粒子群　出處：《浙江大學》2014年博士論文　論文類型：學位論文

【摘要】：隨著產(chǎn)業(yè)化發(fā)展和計算機技術的進步,當前社會進入了大數(shù)據(jù)時代,出現(xiàn)大量具有維度更高、參數(shù)耦合更強、數(shù)據(jù)量幾何級數(shù)增長等特征的工程問題。傳統(tǒng)的確定性優(yōu)化算法對此類問題求解有較大局限性,而粒子群(Particle Swarm Optimization, PSO)和骨干粒子群(Bare Bones PSO, BBPSO)算法由于對目標函數(shù)要求低、理論與實現(xiàn)簡潔、可并行計算、優(yōu)化性能良好等特征,自提出以來便獲得了廣泛關注。然而算法以下三點主要問題需要解決：早熟問題廣泛存在各種PSO及BBPSO中,同時求解不穩(wěn)定,算法魯棒性有待提高；算法并非全局收斂；針對算法求解特性的理論研究過少。 本文針對BBPSO的這些問題及其在工程設計中的應用進行了研究,主要內(nèi)容如下： (一)從最優(yōu)化問題的求解入手,闡述了PSO的研究背景,詳細介紹了BBPSO的發(fā)展及算法理論研究、改進研究和應用情況。在對幾種典型BBPSO分析的基礎上總結了BBPSO的一般形式。 (二)分析了BBPSO算法的客觀性和求解特性。針對BBPSO算法的兩種不同實現(xiàn),分別分析了其平移特性、旋轉特性和粒子多樣性。本文提出并從理論上論證了Ⅰ型實現(xiàn)粒子直線化運動現(xiàn)象和Ⅱ型實現(xiàn)粒子傾向于沿坐標軸運動現(xiàn)象,證明了幾種主流分布下的Ⅱ型BBPSO都有坐標軸偏向現(xiàn)象。在這些結論的指導下給出了算法的求解特性以及應用建議。 (三)提出并詳細分析了一種基于剪枝策略的骨干粒子群算法(記NPSO)。算法使用了一個基于粒子多樣性改進的進化方程,方程中包含一個控制粒子程度的參數(shù)。從理論上分析了能確保群體收斂的參數(shù)范圍并通過實驗驗證了理論分析；基于隨機優(yōu)化算法收斂性判斷準則證明了新方程是全局收斂的；詳細描述并從理論上分析了剪枝策略對求解性能的影響,給出了能同時改善算法全局探索能力和局部開發(fā)能力的剪枝策略要求。最后標準測試函數(shù)實驗結果表明算法性能性能相比幾種經(jīng)典PSO算法有顯著提升。 (四)研究了基于NPSO算法的電力變壓器優(yōu)化設計問題。詳細闡述了變壓器優(yōu)化設計問題,建立了電磁優(yōu)化設計的數(shù)學模型,歸納了四種常用的目標函數(shù)以及三類約束條件,并據(jù)此分析了問題的解空間。采用NPSO算法實施求解,并提出一種新的約束處理方法以處理多重約束問題。此外設計了一種針對低復雜度變壓器設計優(yōu)化求解的枚舉類算法,以該算法和完全枚舉法作為對照,指出NPSO算法具有優(yōu)異的求解性能。 (五)研究了基于NPSO算法的大型電力變壓器油箱強度分析。采用有限元方法建立了變壓器油箱模型,基于等價彈簧模型模擬上下節(jié)油箱連接螺桿在壓力下的運動,提升了模型計算精度。以實驗數(shù)據(jù)為基礎,用NPSO算法擬合得到彈簧的彈性系數(shù)。數(shù)值計算結果證實了本文模型的有效性。以該計算模型分析了常用兩種油箱類型結構形變和應力,給出最大形變和應力的分布,提出設計應注意要點。
[Abstract]:With the development of industrialization and the development of computer technology, the society has entered the era of big data, with the emergence of a large number of higher dimensions, stronger coupling parameters, engineering characteristics of data geometric growth. The deterministic optimization algorithm for solving this problem has great limitation of traditional particle swarm (Particle Swarm, Optimization. PSO (Bare) and the backbone of the particle swarm Bones PSO, BBPSO) algorithm because of low requirement for the objective function, the theory and implementation of simple, parallel computing, optimization and good performance characteristics, since it is proposed to get wide attention. However, the algorithm the following three main problems to be solved: premature problem widely exists in a variety of PSO and BBPSO at the same time, to solve the instability, the robustness of the algorithm needs to be improved; the algorithm is global convergence; algorithm for characteristics of theoretical research less.
In this paper, the problems of BBPSO and their application in engineering design are studied. The main contents are as follows:
(1) starting from the solution of optimization problems, it expounds the research background of PSO, introduces the development of BBPSO and algorithm theory, and improves the research and application. It summarizes the general form of BBPSO based on several typical BBPSO analysis.
(two) analyzes the objectivity and solution characteristics of BBPSO algorithm. According to the two different implementations of BBPSO algorithm, analyzes their characteristics and characteristics of translation, rotating particle diversity. This paper theoretically demonstrates the type of particle linear movement phenomena and type II particles tend to realize the phenomenon along the axis exercise, that type II BBPSO distribution has several main axis deviation phenomenon. In these conclusions under the guidance of the proposed algorithm and the characteristics of the application suggestion.
(three) this paper proposes a novel backbone particle swarm algorithm based on pruning strategy (NPSO). The algorithm uses a particle equation based on evolutionary diversity improvement, including a parameter control particle degree equation. From the theoretical analysis to ensure that the parameters converge and verified by experiments theoretical analysis; stochastic optimization algorithm convergence criterion proved that the new equation is based on global convergence; detailed description and analysis of the effects of pruning strategies on solving performance theoretically, gives the algorithm can improve the global exploration ability and exploitation ability of the pruning strategies. The experiment results show that the performance of the standard test functions the performance of the algorithm compared with several classical PSO algorithm has significantly improved.
(four) study on the optimum design of power transformer based on NPSO algorithm. This paper expounds the optimization design problem of transformer, establishes a mathematical model of electromagnetic optimization design, summarizes four kinds of objective function and three types of constraint conditions, and analyses the solution space of the problem by using NPSO algorithm. The implementation solution, and put forward a a new method to handle the constraints of multiple constraints. In addition to design a low complexity algorithm for enumeration class transformer design optimization, the algorithm and the complete enumeration method as control, and points out that the NPSO algorithm has excellent performance.
(five) on the analysis of tank strength of large power transformer based on NPSO algorithm. A transformer model by the finite element method, simulation of equivalent spring model on oil tank connecting screw under pressure based on motion, improve the calculation precision of the model. Based on the experimental data, obtained the elasticity of the spring by NPSO algorithm. The numerical results confirm the validity of this model. The calculation model of the two kinds of common types of tank structure deformation and stress are given, the maximum deformation and stress distribution, the design should pay attention to the point.

【學位授予單位】：浙江大學
【學位級別】：博士
【學位授予年份】：2014
【分類號】：TM41;TP18

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本文編號：1556811