本文選題：Web服務(wù)組合 + 全局QoS優(yōu)化��； 參考：《南京財(cái)經(jīng)大學(xué)》2014年碩士論文

【摘要】：Web服務(wù)近年以來一直處于高速發(fā)展階段,能夠提供相同或者相似功能的Web服務(wù)數(shù)量也越來越多,因而關(guān)于Web服務(wù)組合QoS的相關(guān)研究也越來越多�；赒oS的Web服務(wù)組合問題是典型的NP難題,當(dāng)Web服務(wù)組合的候選服務(wù)數(shù)量規(guī)模較大時,如何進(jìn)行服務(wù)組合的選擇已經(jīng)變得尤為復(fù)雜。本文針對現(xiàn)有解決Web服務(wù)組合QoS優(yōu)化問題的不足之處,主要從以下幾個方面做了相應(yīng)的工作。首先,分析了以往求解Web服務(wù)組合QoS優(yōu)化問題時存在的不足。大多數(shù)方法在處理此類問題時,往往將各個QoS按照一定的慣性權(quán)疊加或通過效應(yīng)函數(shù)將多目標(biāo)優(yōu)化問題轉(zhuǎn)換成為一個單目標(biāo)優(yōu)化問題,這些方法雖然在一定程度上解決了多個目標(biāo)無法協(xié)調(diào)的問題,但是求解結(jié)果并不能客觀反映用戶的真實(shí)需要。所以本文在解決Web服務(wù)組合QoS優(yōu)化上,考慮了將多個目標(biāo)同時作為一個目標(biāo)向量來考慮,將每個目標(biāo)的具體的量值作為評價結(jié)果考慮。由于多目標(biāo)優(yōu)化問題的本質(zhì)特征,多個目標(biāo)互相之間往往很難同時達(dá)到最優(yōu),因而多目標(biāo)優(yōu)化問題的處理方式不再像單目標(biāo)優(yōu)化問題那樣去簡單的求取最優(yōu)解。為此引入了Pareto解集的概念,將多目標(biāo)優(yōu)化求解過程與Pareto支配結(jié)合起來,使得求得結(jié)果具有很強(qiáng)的Pareto支配性,并將這些支配性很強(qiáng)的解作為優(yōu)化的結(jié)果。其次,針對特定的Web服務(wù)組合QoS優(yōu)化問題,通過對標(biāo)準(zhǔn)粒子群算法的分析,在其基礎(chǔ)上進(jìn)行了改進(jìn)。由于標(biāo)準(zhǔn)粒子群算法主要適用于求解連續(xù)空間上的優(yōu)化問題,而Web服務(wù)組合QoS優(yōu)化問題是典型的離散型空間求解問題,所以將標(biāo)準(zhǔn)粒子群算法改進(jìn)成為離散型粒子群算法,將原來粒子的飛行變?yōu)橄鄳?yīng)的跳動方式。針對粒子群算法的容易陷入局部收斂的特性,將遺傳算法的變異策略引入到新的公式中,同時將蟻群算法的信息素的思想應(yīng)用的新的粒子群公式中,從而使得新的粒子群算法兼具上述兩者算法的優(yōu)點(diǎn)。最后以一個典型的Web服務(wù)組合模型中的順序結(jié)構(gòu)模型為例,利用本文提出的新的粒子群算法,采用Pareto推優(yōu)的方式,通過仿真實(shí)驗(yàn)來求解Web服務(wù)組合QoS多目標(biāo)優(yōu)化問題。對實(shí)驗(yàn)結(jié)果進(jìn)行了相關(guān)分析,得到Web服務(wù)組合QoS優(yōu)化問題求解速度的主要影響因素,并且通過與已有的方法對比,驗(yàn)證了本文方法的可行性和有效性,突出了本文方法在一定情況下求解過程中效率上的優(yōu)勢。從而為Web服務(wù)組合QoS優(yōu)化問題提供了一種可行的求解方式。
[Abstract]:Web services have been developing at a high speed in recent years, and more and more Web services can provide the same or similar functions. Therefore, there are more and more researches on QoS composition of Web services.The Web service composition problem based on QoS is a typical NP problem. When the number of candidate services for Web service composition is large, how to select service composition has become more and more complicated.In order to solve the problem of Web service composition QoS optimization, this paper does some work in the following aspects.Firstly, the shortcomings of solving Web service composition QoS optimization problems are analyzed.When dealing with this kind of problem, most methods often superposition each QoS according to a certain inertia weight or convert the multi-objective optimization problem into a single-objective optimization problem through the effect function.Although these methods to some extent solve the problem of multiple objectives can not be coordinated, but the results of the solution can not objectively reflect the real needs of users.In order to solve the problem of QoS optimization of Web services composition, this paper considers multiple objectives as an objective vector at the same time, and takes the specific value of each goal as the evaluation result.Because of the essential characteristics of multi-objective optimization problems, it is difficult for multiple objectives to achieve optimization simultaneously, so the multi-objective optimization problem is no longer as simple as the single-objective optimization problem to obtain the optimal solution.In this paper, the concept of Pareto solution set is introduced, the multi-objective optimization solution process is combined with Pareto domination, so that the results have strong Pareto dominance, and these strong dominating solutions are regarded as the results of optimization.Secondly, based on the analysis of the standard particle swarm optimization (Swarm Optimization) algorithm, an improved Web service composition QoS optimization problem is proposed.Because the standard particle swarm optimization algorithm is mainly suitable for solving the optimization problem in continuous space and the Web service composition QoS optimization problem is a typical discrete space optimization problem, the standard particle swarm optimization algorithm is improved to discrete particle swarm optimization algorithm.Turn the original particle's flight into a corresponding pulsation mode.Aiming at the local convergence of particle swarm optimization algorithm, the mutation strategy of genetic algorithm is introduced into the new formula, and the pheromone of ant colony algorithm is applied to the new particle swarm optimization formula.Therefore, the new particle swarm optimization algorithm has the advantages of the above two algorithms.Finally, taking the sequential structure model of a typical Web service composition model as an example, using the new particle swarm optimization algorithm proposed in this paper, the multi-objective optimization problem of Web service composition QoS is solved by using the Pareto optimization method and the simulation experiment.Through the correlation analysis of the experimental results, the main factors influencing the solution speed of the Web service composition QoS optimization problem are obtained, and the feasibility and effectiveness of this method are verified by comparing with the existing methods.The advantages of the proposed method in solving the problem are highlighted.It provides a feasible solution for Web service composition QoS optimization problem.
【學(xué)位授予單位】：南京財(cái)經(jīng)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP393.09

【參考文獻(xiàn)】

相關(guān)期刊論文 前1條

1 張成文;蘇森;陳俊亮;;基于遺傳算法的QoS感知的Web服務(wù)選擇[J];計(jì)算機(jī)學(xué)報;2006年07期

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本文編號：1771747