本文選題：多播路由　切入點(diǎn)：云　出處：《遼寧科技大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著信息時(shí)代的高速發(fā)展，網(wǎng)絡(luò)生活已經(jīng)完全融入到人們的日常生活中，那么在適應(yīng)這種生活模式的同時(shí)，人們也在尋求更加舒適的體驗(yàn)和享受，QoS作為一種衡量標(biāo)準(zhǔn)應(yīng)運(yùn)而生。對(duì)于在QoS基礎(chǔ)上尋求使用最小花費(fèi)來獲取最大的網(wǎng)絡(luò)資源已經(jīng)成為網(wǎng)絡(luò)研究的一個(gè)重大方向。本文針對(duì)蟻群算法自身的缺陷，在分析了蟻群算法在解決路由問題中存在的路由選擇時(shí)間長(zhǎng)，全局收斂能力差和容易陷入局部最優(yōu)等問題，對(duì)原有的基本蟻群算法使用云模型進(jìn)行優(yōu)化，提高了算法效率。 論文采用QoS多播路由模型進(jìn)行模擬，在論文中間部分還對(duì)其相關(guān)知識(shí)進(jìn)行敘述，主要包括多播相關(guān)技術(shù)、QoS約束，QoS算法的現(xiàn)狀等。論文采用無線路由來構(gòu)建多播路由算法的數(shù)學(xué)模型，結(jié)合蟻群算法和云模型來優(yōu)化網(wǎng)絡(luò)開銷，通過最后的數(shù)據(jù)驗(yàn)證，從而得出算法在理論上的可行性。 由于螞蟻群體的正反饋機(jī)制，導(dǎo)致算法容易陷入局部最優(yōu)，論文改進(jìn)了信息素?fù)]發(fā)策略，，使用云模型作動(dòng)態(tài)自適應(yīng)規(guī)劃，動(dòng)態(tài)地調(diào)整局部信息素更新策略，提高了算法的有效性。在整體信息素更新策略上采用最新的最優(yōu)最差路徑更新規(guī)則，提高了算法的全局收斂性。仿真結(jié)果表明：CACA(云蟻群算法)在解決路由問題上是有效的，和傳統(tǒng)的QoS多播路由算法相比在收斂性、收斂速度上都有很大的提高，代價(jià)樹也得到優(yōu)化。
[Abstract]:With the rapid development of the information age, network life has been fully integrated into people's daily life, so while adapting to this mode of life, People are also looking for more comfortable experience and enjoyment of QoS as a standard of measurement. Seeking to use minimum cost to obtain the largest network resources based on QoS has become a major direction of network research. In this paper, aiming at the defects of ant colony algorithm, In this paper, the problems of long routing time, poor global convergence and easy to fall into local optimization are analyzed. The cloud model is used to optimize the original basic ant colony algorithm, and the efficiency of the algorithm is improved. In this paper, the QoS multicast routing model is used to simulate, and the related knowledge is described in the middle of the paper. This paper uses wireless routing to construct the mathematical model of multicast routing algorithm, combines ant colony algorithm and cloud model to optimize the network overhead, and finally verifies the data. The theoretical feasibility of the algorithm is obtained. Due to the positive feedback mechanism of ant population, the algorithm is easy to fall into local optimum. In this paper, the pheromone volatilization strategy is improved, the cloud model is used for dynamic adaptive programming, and the local pheromone updating strategy is dynamically adjusted. The global convergence of the algorithm is improved by adopting the latest optimal worst path updating rules in the overall pheromone updating strategy. The simulation results show that the cloud ant colony algorithm (CACA) is effective in solving routing problems. Compared with the traditional QoS multicast routing algorithm, the convergence rate and the cost tree are improved greatly.
【學(xué)位授予單位】：遼寧科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP393.09

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本文編號(hào)：1649034