本文選題：Web服務(wù) + Web服務(wù)選取��； 參考：《吉林大學(xué)》2014年博士論文

【摘要】：本文主要研究內(nèi)容為當(dāng)前Web服務(wù)領(lǐng)域中的幾個熱點(diǎn)問題，包括Web服務(wù)選取方法、多等級Web服務(wù)部署問題以及Web服務(wù)組合運(yùn)行時容錯處理和異�；謴�(fù)方法及策略的研究。 本文中首先提出了一種改進(jìn)的人工蜂群算法用來求解服務(wù)選取問題。結(jié)合遺傳操作設(shè)計雇用蜂覓食及偵查蜂策略，并針對服務(wù)選取問題的特點(diǎn)提出了基于效用值及違反約束度的食物源評價方法及基于鄰域的隨機(jī)貪心觀察蜂覓食策略，進(jìn)而建立求解該問題的人工蜂群算法優(yōu)化模型，在此基礎(chǔ)上實(shí)現(xiàn)了求解服務(wù)選取問題的混合人工蜂群算法-GABC。 其次提出了一種基于協(xié)同過濾推薦的混合式Web服務(wù)選取方法，將Web服務(wù)請求者的個人興趣偏好和相似用戶群體的經(jīng)驗(yàn)相結(jié)合，利用推薦技術(shù)對具有相同功能和不同Qos屬性值的Web服務(wù)進(jìn)行選取。 再次提出了一種改進(jìn)的遺傳算法用來求解多等級服務(wù)部署問題。在選擇過程中引入了個體被支配強(qiáng)度，通過將個體的支配強(qiáng)度和被支配強(qiáng)度結(jié)合到一起建立對個體的評價策略，進(jìn)而對效用函數(shù)進(jìn)行重新定義，，并設(shè)計了基于概率的交叉策略及結(jié)合局部搜索的個體變異策略和根據(jù)評價結(jié)果進(jìn)行環(huán)境選擇及生成個體的交叉概率方法。此外還設(shè)計新的局部搜索策略并將其融入到變異策略中，提高了變異操作的有效性。 最后給出了一種基于服務(wù)冗余和約束冗余的Web服務(wù)組合運(yùn)行時容錯處理和異常恢復(fù)策略。通過在組合規(guī)劃階段為服務(wù)流程中每個抽象成員服務(wù)建立后備服務(wù)組和將約束分解到每個抽象成員服務(wù)上的方法，為運(yùn)行時的容錯處理和異�；謴�(fù)提供了一種以盡可能小的代價恢復(fù)服務(wù)組合運(yùn)行并盡量保留已執(zhí)行部分結(jié)果的處理策略。在此基礎(chǔ)上，進(jìn)一步給出了服務(wù)組件庫的建立和維護(hù)策略及其算法。 本文中針對Web服務(wù)領(lǐng)域中幾個關(guān)鍵問題進(jìn)行了研究和探討，所做的主要研究工作和研究成果在提高web服務(wù)和服務(wù)組合的可靠性和效率等方面具有一定的意義。
[Abstract]:This paper focuses on several hot topics in the field of Web services, including the selection of Web services, the deployment of multilevel Web services, and the research of fault tolerance and exception recovery methods and strategies in Web service composition runtime.
This paper first presents an improved artificial bee colony algorithm is used to solve the problem of the service selection. Combined with the genetic operators employed bee and bee foraging investigation strategy, according to the characteristics of the service selection problem and put forward the utility value and evaluation method of constraint violation of food source and neighborhood random greedy bee foraging strategy based on observation, optimization model and the establishment of artificial bee colony algorithm for solving this problem, based on the realization of the -GABC. hybrid artificial bee colony algorithm selection problem solving service
Secondly, a hybrid Web service selection method based on collaborative filtering recommendation is proposed, which combines the Web service requester's personal interest preference with the experience of similar user groups, and uses recommendation technology to select Web services with the same function and different Qos attribute values.
Again, this paper proposes an improved genetic algorithm for solving multi class service deployment problem. In the selection process using individual dominated by strength, will dominate and strength of individual dominated strength combined to establish evaluation strategies for individuals together, and the utility function for re defined and designed based on the crossover probability and combined with the local search strategy according to the individual variation and crossover probability evaluation results of environmental selection and generation of the individual. In addition, the design of a new local search strategy and put it into the mutation strategy, the effectiveness of high mutation operation.
Finally, a Web service composition service redundancy and redundant constraints based on the fault-tolerant processing and exception recovery strategy. Through each service process Abstract member services build up a reserve service group and the Constraint Decomposition to each service for abstract members in combinatorial planning stage, to run the fault-tolerant processing and exception recovery a recovery service combination operation and to retain some results processing strategy has been performed with as little as possible cost. On this basis, further gives the service component database establishment and the maintenance strategy and algorithm.
In this paper, several key issues in the field of Web services are studied and discussed. The main research work and research results are of great significance in improving the reliability and efficiency of Web services and service composition.

【學(xué)位授予單位】：吉林大學(xué)
【學(xué)位級別】：博士
【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2條

1 夏亞梅;程渤;陳俊亮;孟祥武;劉棟;;基于改進(jìn)蟻群算法的服務(wù)組合優(yōu)化[J];計算機(jī)學(xué)報;2012年02期

2 李曉磊,邵之江,錢積新;一種基于動物自治體的尋優(yōu)模式:魚群算法[J];系統(tǒng)工程理論與實(shí)踐;2002年11期

本文編號：1772098