本文選題：人工免疫 + 粒群優(yōu)化��； 參考：《鄭州大學(xué)》2014年碩士論文

【摘要】：隨著近年來計算機應(yīng)用的推廣，人們對網(wǎng)絡(luò)流量的需求以及網(wǎng)絡(luò)規(guī)模的要求也越來越高，網(wǎng)絡(luò)的飛速發(fā)展導(dǎo)致了時有網(wǎng)絡(luò)擁塞現(xiàn)象的發(fā)生。為了保證網(wǎng)絡(luò)的正常運行，就需要更高的網(wǎng)絡(luò)服務(wù)質(zhì)量（QoS）來滿足網(wǎng)絡(luò)發(fā)展的需求。網(wǎng)絡(luò)擁塞的存在制約著網(wǎng)絡(luò)的發(fā)展和應(yīng)用，采取合理的措施來預(yù)防和控制網(wǎng)絡(luò)擁塞的發(fā)生有著極其重要意義。用傳統(tǒng)優(yōu)化的方法來控制網(wǎng)絡(luò)擁塞雖然在不斷的完善，，但是總存在這樣或者那樣的問題。近年來，針對傳統(tǒng)優(yōu)化算法的不足之處，國內(nèi)外的學(xué)者將智能優(yōu)化算法應(yīng)用到網(wǎng)絡(luò)擁塞控制當(dāng)中，通過智能優(yōu)化算法來實現(xiàn)網(wǎng)絡(luò)擁塞的控制成為了當(dāng)前的一個研究熱點。智能優(yōu)化算法通過無意識的尋優(yōu)行為來適應(yīng)生存環(huán)境和優(yōu)化生存狀態(tài)的一種新型優(yōu)化算法，到目前為止已經(jīng)出現(xiàn)了多種智能優(yōu)化算法。本文將人工免疫算法和粒子群優(yōu)化算法結(jié)合在了一起應(yīng)用到網(wǎng)絡(luò)擁塞控制當(dāng)中，具體的研究內(nèi)容如下所述： （1）首先對網(wǎng)絡(luò)擁塞現(xiàn)象、網(wǎng)絡(luò)擁塞的成因、網(wǎng)絡(luò)擁塞控制機制及其網(wǎng)絡(luò)擁塞控制算法進行了分析，并對仿真軟件NS2的應(yīng)用進行了分析。 （2）建立了研究網(wǎng)絡(luò)擁塞控制的網(wǎng)絡(luò)拓撲模型，并對網(wǎng)絡(luò)拓撲模型和網(wǎng)絡(luò)路由進行了分析和探討。在對網(wǎng)絡(luò)路徑優(yōu)化分析的基礎(chǔ)上提出了網(wǎng)絡(luò)擁塞路徑優(yōu)化問題。 （3）將人工免疫和粒子群優(yōu)化算法結(jié)合給出了一種免疫粒群優(yōu)化算法。該算法將免疫算法中的免疫信息處理機制融合到粒子群優(yōu)化算法當(dāng)中，根據(jù)粒子群算法的收斂速度快的特點，利用人工免疫算法的特征多樣性避免粒子群優(yōu)化算法陷入局部解，提高了粒子群優(yōu)化算法的后期收斂速度。 （4）給出了一種基于免疫粒群優(yōu)化的網(wǎng)絡(luò)擁塞控制算法。以資源的消耗和負載均衡分布為網(wǎng)絡(luò)路徑優(yōu)化目標，在滿足帶寬、時延、費用、等多項指標的前提下，使負載盡量均衡分布在有寬裕資源的鏈路上，提高了網(wǎng)絡(luò)的吞吐量和傳輸效率，可有效的實現(xiàn)網(wǎng)絡(luò)擁塞的優(yōu)化控制。仿真結(jié)果表明了算法的有效性和可靠性。
[Abstract]:With the popularization of computer application in recent years, the demand for network traffic and network scale is becoming higher and higher. The rapid development of network has led to the phenomenon of network congestion. In order to ensure the normal operation of the network, a higher quality of service (QoS) is needed to meet the needs of network development. The existence of network congestion restricts the development and application of network. It is of great significance to take reasonable measures to prevent and control network congestion. Though the traditional optimization method is used to control the network congestion, there are always some problems. In recent years, in view of the shortcomings of traditional optimization algorithms, scholars at home and abroad apply intelligent optimization algorithm to network congestion control. The intelligent optimization algorithm to achieve network congestion control has become a hot research topic. Intelligent optimization algorithm is a new kind of optimization algorithm which adapts to the living environment and optimizes the living state by unconscious optimization behavior. Up to now, there have been many kinds of intelligent optimization algorithms. In this paper, the artificial immune algorithm and particle swarm optimization algorithm are combined in the network congestion control, the specific research content is as follows: Firstly, the phenomenon of network congestion, the cause of network congestion, the mechanism of network congestion control and the algorithm of network congestion control are analyzed, and the application of simulation software NS2 is analyzed. (2) the network topology model of network congestion control is established, and the network topology model and network routing are analyzed and discussed. Based on the analysis of network path optimization, the problem of network congestion path optimization is proposed. 3) an immune particle swarm optimization algorithm is proposed by combining artificial immune algorithm with particle swarm optimization algorithm. The immune information processing mechanism of the immune algorithm is integrated into the particle swarm optimization algorithm. According to the fast convergence speed of the particle swarm optimization algorithm, the artificial immune algorithm features diversity to avoid the particle swarm optimization algorithm into a local solution. The late convergence rate of PSO is improved. A network congestion control algorithm based on immune particle swarm optimization is proposed. Taking the resource consumption and load balance distribution as the network path optimization goal, under the premise of satisfying the bandwidth, delay, cost, and so on, the load can be distributed on the link with abundant resources as far as possible. The throughput and transmission efficiency of the network are improved, and the optimal control of network congestion can be realized effectively. Simulation results show that the algorithm is effective and reliable.
【學(xué)位授予單位】：鄭州大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP393.06;TP18

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