【摘要】：近年來智能計(jì)算在人類生活中各個(gè)方面都展示了其不容忽視的作用,智能計(jì)算輔助人類進(jìn)行高效的生產(chǎn),為工業(yè)生產(chǎn)、科技發(fā)展及人類社會進(jìn)步作出積極的貢獻(xiàn).為更好的解決社會生產(chǎn)生活所面臨的最優(yōu)化問題,模仿自然界生物進(jìn)化過程的眾多隨機(jī)啟發(fā)式仿生算法隨之應(yīng)運(yùn)而生,這些仿生算法在解決復(fù)雜問題方面有著顯著的成效.為使仿生算法發(fā)揮更好的性能,從理論分析的角度抽象出仿生算法內(nèi)在的規(guī)律是迫切需要進(jìn)行研究的方向.差分演化算法是目前應(yīng)用較為廣泛的隨機(jī)啟發(fā)式算法,因其所具有效果顯著、空間復(fù)雜性低等特點(diǎn)使其受到了廣泛的關(guān)注.本文基于并行性特征這一原理分析差分演化算法在迭代過程中的特點(diǎn),利用圖論的方式展現(xiàn)出算法所具有的內(nèi)在特征,利用這一理論方法分析出差分演化算法所具有的穩(wěn)定性及強(qiáng)健性的原因,從算法的進(jìn)化過程中分析出算法所具有并行特征的強(qiáng)弱對算法的影響.本文從理論分析差分演化算法的并行性特征出發(fā),分析算法在進(jìn)化過程中的特點(diǎn),關(guān)注算法并行特征對算法效果的影響.本文的研究成果主要有以下幾方面:(1)從并行性的思想角度出發(fā),仿生算法展現(xiàn)出越來越杰出的搜索高效性和信息共享性的特征,算法中種群進(jìn)化行為越來越趨于群體性和并行性,利用種群迭代過程中所包含的并行性特征行為作為分析算法性能的出發(fā)點(diǎn).(2)本文將基于圖論的研究方法分析差分演化算法的并行特征,圖論作為數(shù)學(xué)科學(xué)中的一個(gè)重要的分支,將有效的展現(xiàn)種群個(gè)體進(jìn)化過程中的個(gè)體間的關(guān)系,更好的輔助算法理論進(jìn)行分析研究.(3)從隨機(jī)啟發(fā)式算法進(jìn)化過程的研究來看,仿生算法的理論分析至今都是研究的薄弱環(huán)節(jié),利用圖論論證生成路徑的數(shù)量對于算法性能的影響,在種群信息共享的基礎(chǔ)上,分析個(gè)體所產(chǎn)生新搜索方向,成為分析算法內(nèi)在特征的量化指標(biāo).本文的研究成果不僅豐富了隨機(jī)啟發(fā)式算法的理論研究成果,而且得到算法的并行性程度與算法性能之間存在相關(guān)性的結(jié)論.
[Abstract]:In recent years, intelligent computing has played an important role in all aspects of human life. Intelligent computing can assist human to carry out efficient production and make positive contributions to industrial production, scientific and technological development and the progress of human society. In order to solve the optimization problem of social production and life better, many random heuristic bionic algorithms, which mimic the evolution process of natural organisms, emerge as the times require. These bionic algorithms have remarkable results in solving complex problems. In order to improve the performance of bionic algorithm, it is urgent to abstract the inherent law of bionic algorithm from the angle of theoretical analysis. Differential evolution algorithm (DEA) is a widely used stochastic heuristic algorithm, which has attracted wide attention because of its remarkable effect and low spatial complexity. Based on the principle of parallelism, this paper analyzes the characteristics of the differential evolution algorithm in the iterative process, and shows the inherent characteristics of the algorithm by means of graph theory. The reason of stability and robustness of the differential evolution algorithm is analyzed by using this method. The influence of the strength of the parallel characteristic of the algorithm on the algorithm is analyzed from the evolutionary process of the algorithm. Based on the theoretical analysis of the parallelism of the differential evolution algorithm, this paper analyzes the characteristics of the algorithm in the evolution process, and pays attention to the influence of the parallel feature of the algorithm on the effectiveness of the algorithm. The main research results of this paper are as follows: (1) from the perspective of parallelism, bionic algorithms show more and more outstanding features of search efficiency and information sharing. The evolutionary behavior of population in the algorithm tends to be group and parallelism more and more. The parallel characteristic behavior included in the population iteration process is used as the starting point to analyze the performance of the algorithm. (2) in this paper, the parallel features of the differential evolution algorithm are analyzed based on graph theory. As an important branch of mathematical science, graph theory will effectively show the relationship between individuals in the evolution of individual population. (3) from the research of evolutionary process of stochastic heuristic algorithm, the theoretical analysis of bionic algorithm is still the weak link. The effect of the number of generated paths on the performance of the algorithm is demonstrated by using graph theory. On the basis of population information sharing, the new search direction produced by individuals is analyzed, which becomes a quantitative index for analyzing the inherent characteristics of the algorithm. The research results of this paper not only enrich the theoretical research results of stochastic heuristic algorithm, but also obtain the conclusion that the degree of parallelism of the algorithm is related to the performance of the algorithm.
【學(xué)位授予單位】：廣東工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP18;O157.5

【參考文獻(xiàn)】

相關(guān)期刊論文 前6條

1 黃翰;徐威迪;張宇山;林智勇;郝志峰;;基于平均增益模型的連續(xù)型(1+1)進(jìn)化算法計(jì)算時(shí)間復(fù)雜性分析[J];中國科學(xué):信息科學(xué);2014年06期

2 黃翰;林智勇;郝志峰;張宇山;李學(xué)強(qiáng);;基于關(guān)系模型的進(jìn)化算法收斂性分析與對比[J];計(jì)算機(jī)學(xué)報(bào);2011年05期

3 張金標(biāo);;并行設(shè)計(jì)混合迭代過程規(guī)劃的遺傳算法[J];現(xiàn)代制造工程;2008年07期

4 黃翰;郝志峰;吳春國;秦勇;;蟻群算法的收斂速度分析[J];計(jì)算機(jī)學(xué)報(bào);2007年08期

5 郭振宇;程博;葉敏;康龍?jiān)?曹秉剛;;一種并行混沌差異演化算法[J];西安交通大學(xué)學(xué)報(bào);2007年03期

6 徐宗本,聶贊坎,張文修;遺傳算法的幾乎必然強(qiáng)收斂性——鞅方法[J];計(jì)算機(jī)學(xué)報(bào);2002年08期

，

本文編號：2412035