發(fā)布時(shí)間：2023-02-17 10:29

　　實(shí)驗(yàn)是人們認(rèn)識(shí)和改造世界的重要手段,今天的生活中無(wú)處不存在試驗(yàn)。統(tǒng)計(jì)試驗(yàn)可以抽象的理解為安排一系列通過(guò)不同的輸入獲得不同輸出的檢驗(yàn)活動(dòng)。,通過(guò)分析這種變化之間的關(guān)系建立統(tǒng)計(jì)模型。而試驗(yàn)設(shè)計(jì)則是試著用最節(jié)約的試驗(yàn)次數(shù)(輸入)獲得盡可能的信息。一個(gè)試驗(yàn)的所有可能輸入的輸入組合可能非常多,全部都進(jìn)行試驗(yàn)通常不現(xiàn)實(shí);很多時(shí)候也沒(méi)有必要對(duì)所有的試驗(yàn)組合進(jìn)行試驗(yàn),我們只需要對(duì)部分組合進(jìn)行試驗(yàn)。對(duì)于這個(gè)部分如何選擇的問(wèn)題的不同回答導(dǎo)出了許多不同的試驗(yàn)理念和方法�？臻g填充試驗(yàn)是一種盡量使試驗(yàn)點(diǎn)均勻散布于目標(biāo)實(shí)驗(yàn)區(qū)域的試驗(yàn)設(shè)計(jì)方法。如:超級(jí)拉丁方設(shè)計(jì)、最小最大、最大最小距離設(shè)計(jì)、均勻設(shè)計(jì)。偏差是評(píng)價(jià)一個(gè)設(shè)計(jì)在目標(biāo)區(qū)域內(nèi)的均勻性的最為廣泛和流行的準(zhǔn)則。一個(gè)具有最小偏差值的設(shè)計(jì)被稱(chēng)為該偏差準(zhǔn)則下的最優(yōu)設(shè)計(jì)。顯然給定偏差準(zhǔn)則后,如何在海量的備選設(shè)計(jì)中尋找偏差最小的最優(yōu)設(shè)計(jì),隨著試驗(yàn)問(wèn)題越來(lái)越復(fù)雜,這一優(yōu)化問(wèn)題也變的越來(lái)越具有挑戰(zhàn)性。很多專(zhuān)家學(xué)者對(duì)這一優(yōu)化問(wèn)題提出了許多具有深刻洞察力的方法。本文引入近幾年在隨機(jī)優(yōu)化領(lǐng)域被廣泛使用的離散粒子群算法,對(duì)這一算法的不同參數(shù)設(shè)置進(jìn)行了改進(jìn)和優(yōu)化,使該算法能適應(yīng)最優(yōu)空間填...

【文章頁(yè)數(shù)】：100 頁(yè)

【學(xué)位級(jí)別】：博士

【文章目錄】：
ACKNOWLEDGEMENT
Abstract
摘要
List of Abbreviations
CHAPTER ONE:INTRODUCTION
    1.1 Background Information
    1.2 Statement of the problem
    1.3 Objectives
        1.3.1 General objective
        1.3.2 Specific objectives
    1.4 Research Questions
    1.5 Justification of the study
CHAPTER TWO: LITERATURE REVIEW
    2.0 Introduction
    2.1 Design of Experiment
        2.1.1 Basic terms used in Experimental Designs
        2.1.2 Basic Principles of Experimental Designs
        2.1.3 Factorial Designs
    2.2 Uniform Design
        2.2.1 Uniform design as a space filling design
        2.2.2 Uniformity criterion: the discrepancy
            2.2.2.1 The Star Discrepancy
            2.2.2.2 The Local Discrepancy
            2.2.2.3 The Centered L₂ discrepancy
            2.2.2.4 The wrap-around L₂ discrepancy
            2.2.2.5 The Mixture Discrepancy
            2.2.2.6 Symmetric discrepancy
    2.3 Construction of Uniform Design
    2.4 Optimization
        2.4.1 Classification of Optimization Problems
            2.4.1.1 Constrained Optimization
            2.4.1.2 Unconstrained Optimization
            2.4.1.3 Dynamic Optimization
            2.4.1.4 Least Square Optimization
        2.4.2 Optimization Techniques
            2.4.2.1 Local Optimization
            2.4.2. 2 Global Optimization
        2.4.3 Optimization Algorithms
            2.4.3.1 Local search algorithm
            2.4.3.2 Simulating annealing
            2.4.3.3 Genetic algorithm
            2.4.3.4 Stochastic evolutionary algorithms
            2.4.3.5 Threshold accepting
        2.4.4 To Generate Symmetrical And Asymmetrical Uniform Designs From U(n;n^s)
        2.4.5 Uniform Designs with large n
    2.5 Particle Swarm Optimization
        2.5.1 The particle swarm algorithm
            2.5.1.1 Global best PSO
            2.5.1.2 Local best PSO
    2.6 Discrete particle swarm optimization
CHAPTER THREE:METHODOLOGY
    3.1 Discrete particle swarm optimization for the designs matrices
        DPSO FLOW CHART
        3.1.1 Initialization
        3.1.2 The function value
        3.1.3 The learning process
        3.1.4 Accepting strategy
        3.1.5 Update process
CHAPTER FOUR: NUMERICAL RESULTS
    4.0 Introduction
    4.1 The number of designs in the design space
    4.2 Parameters used in the swarm
        4.2.1 The number of initial particles in the swarm
        4.2.2 The number of iterations
        4.2.3 probR
        4.2.4 The constants p and g
    4.3 Comparison with TA
        4.3.1 under WD
        4.3.2 under CD
        4.3.3 under SD
    4.4 Asymmetric and symmetric designs
    4.5 for large n
    4.6 Special cases (orthogonality)
        4.6.1 use of orthogonal array for multilevel designs
        4.6.2 orthogonal and nearly orthogonal Latin hypercube designs for high levelled designs
CHAPTER FIVE:CONCLUSION AND DISCUSSION
Future work
APPENDIX
References



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