發(fā)布時間：2018-01-03 13:12

  本文關(guān)鍵詞：進化計算中的復(fù)雜網(wǎng)絡(luò)動力學(xué)研究　出處：《河北工程大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 復(fù)雜網(wǎng)絡(luò) 進化計算 網(wǎng)絡(luò)動力學(xué) 網(wǎng)絡(luò)結(jié)構(gòu) 種群多樣性

【摘要】：基于達爾文進化論的進化算法在求解問題時,可將問題描述成自然界中種群的進化過程,遵循適者生存的機制,通過種群的不斷進化,求得最優(yōu)解或滿意解。進化計算的研究較早,發(fā)展較成熟,且廣泛應(yīng)用于社會的各個領(lǐng)域。但現(xiàn)有的研究只注重算法的最終結(jié)果或預(yù)測結(jié)果,往往忽視了進化過程中個體之間的關(guān)系和這些關(guān)系隨進化過程的變化,以及該變化關(guān)系對進化結(jié)果和收斂速度的影響。而本文主要針對這個被忽視的問題展開研究。通過研究優(yōu)化過程中個體之間的變化關(guān)系抽象出進化計算形成的網(wǎng)絡(luò)結(jié)構(gòu)中蘊含的復(fù)雜網(wǎng)絡(luò)結(jié)構(gòu),并挖掘出進化計算中的復(fù)雜網(wǎng)絡(luò)動力學(xué)現(xiàn)象。隨著復(fù)雜網(wǎng)絡(luò)理論體系的不斷發(fā)展和研究,基于復(fù)雜網(wǎng)絡(luò)研究的應(yīng)用越來越多。由于算法在迭代過程中,參與進化的個體總數(shù)不變,即網(wǎng)絡(luò)結(jié)構(gòu)中節(jié)點的總數(shù)不變。網(wǎng)絡(luò)結(jié)構(gòu)隨著邊的連接概率動態(tài)變化。網(wǎng)絡(luò)結(jié)構(gòu)的變化影響著網(wǎng)絡(luò)動力學(xué)演化過程,而動力學(xué)的演化過程也影響網(wǎng)絡(luò)邊的動態(tài)重連,網(wǎng)絡(luò)結(jié)構(gòu)與動力學(xué)演化過程之間的動態(tài)作用稱為“共同演化”過程,且受到廣大科研工作者的關(guān)注。這兩個看似完全不同的研究領(lǐng)域,復(fù)雜網(wǎng)絡(luò)和進化計算,兩者之間是否存在某種隱藏的結(jié)構(gòu)關(guān)系,復(fù)雜網(wǎng)絡(luò)動力學(xué)能否描述算法的優(yōu)化過程,將成為未來研究的一大亮點。本文討論了一個完全不同領(lǐng)域的相互交叉研究:進化計算中是否蘊含著復(fù)雜網(wǎng)絡(luò)動力學(xué)現(xiàn)象。首先研究一般進化計算及改進算法的優(yōu)化過程;然后分析優(yōu)化過程中各個體之間的變化關(guān)系;再用復(fù)雜網(wǎng)絡(luò)模型將個體之間的變化關(guān)系進行動力學(xué)過程描述,并討論其蘊含的復(fù)雜網(wǎng)絡(luò)結(jié)構(gòu);最后分析該變化對進化結(jié)果和收斂速度的影響,并提出算法的改進意見,提高種群的多樣性。實驗結(jié)果表明進化計算的優(yōu)化過程可以用復(fù)雜網(wǎng)絡(luò)動力學(xué)描述,利用復(fù)雜網(wǎng)絡(luò)理論可有效控制并改進進化算法。本文的研究對于復(fù)雜網(wǎng)絡(luò)的深入研究以及進化計算的改進、優(yōu)化和控制等應(yīng)用方面具有一定的理論意義和應(yīng)用價值。
[Abstract]:The evolutionary algorithm based on Darwin's theory of evolution can describe the problem as the evolution process of population in nature, follow the mechanism of survival of the fittest, and continue to evolve through population. The research of evolutionary computing is earlier, more mature, and widely used in various fields of society. However, the existing research only focuses on the final results of the algorithm or the prediction results. The relationships between individuals in the evolution process and their changes with the evolution process are often ignored. And the influence of the change relation on the evolution result and convergence rate. This paper mainly focuses on the neglected problem. By studying the variation relation between individuals in the optimization process, the network formed by evolutionary computation is abstracted. The complex network structure contained in the network structure. With the development and research of complex network theory system, more and more applications based on complex network are found. Because the algorithm is in the iterative process. The total number of individuals involved in evolution is the same, that is, the total number of nodes in the network structure is unchanged. The network structure changes dynamically with the connection probability of the edge. The network structure changes affect the evolution process of the network dynamics. The dynamical evolution process also affects the dynamic reconnection of the network edge. The dynamic interaction between the network structure and the dynamic evolution process is called "co-evolution" process. These two seemingly different research fields, complex networks and evolutionary computing, have some hidden structural relationship. Can complex network dynamics describe the optimization process of the algorithm. This paper discusses an entirely different field of intersecting research:. Whether there are complex network dynamics phenomena in evolutionary computing. Firstly, the optimization process of general evolutionary computing and improved algorithm is studied. Then the relationship between the individuals in the process of optimization is analyzed. Then the dynamic process of the relationship between individuals is described by using the complex network model, and the complex network structure is discussed. Finally, the influence of the change on the evolution result and convergence rate is analyzed, and the improvement of the algorithm is proposed to improve the diversity of the population. The experimental results show that the optimization process of evolutionary computation can be described by complex network dynamics. The evolutionary algorithm can be effectively controlled and improved by using the complex network theory. The application of optimization and control has certain theoretical significance and application value.
【學(xué)位授予單位】：河北工程大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：O157.5;TP18

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