本文選題：面向服務(wù)軟件　切入點(diǎn)：部署優(yōu)化　出處：《中南大學(xué)學(xué)報(bào)(自然科學(xué)版)》2017年09期

【摘要】：基于根據(jù)動(dòng)態(tài)變化的外部環(huán)境調(diào)整面向服務(wù)軟件的部署方案是提升其運(yùn)行性能、降低運(yùn)行成本的一種有效途徑,提出一種基于多目標(biāo)蟻群算法的MACO-DO,以便在自動(dòng)為面向服務(wù)軟件尋找一組在性能和成本之間作出最優(yōu)權(quán)衡的部署方案。MACO-DO算法是對(duì)傳統(tǒng)多目標(biāo)蟻群算法的一種改進(jìn),引入摒棄精英解策略以避免算法早熟收斂,設(shè)計(jì)1個(gè)局部搜索過程以加快獲得可行解的過程。在Case 1,Case 2和Case 3共3種不同規(guī)模的模擬案例上將提出的MACO-DO算法與P-ACO算法和NSGA-Ⅱ算法進(jìn)行對(duì)比。研究結(jié)果表明:MACO-DO算法在求解問題上具有更好的性能。
[Abstract]:Adjusting the deployment scheme of service-oriented software based on dynamic changing external environment is an effective way to improve its performance and reduce its running cost. This paper presents a multi-objective ant colony algorithm based on MACO-DO.MACO-DO algorithm is an improvement on the traditional multi-objective ant colony algorithm in order to automatically find a set of deployment schemes for service-oriented software that make the best trade-off between performance and cost. The elitist solution strategy is introduced to avoid premature convergence of the algorithm. A local search process was designed to speed up the process of obtaining feasible solutions. The proposed MACO-DO algorithm was compared with P-ACO algorithm and NSGA- 鈪，

本文編號(hào)：1684648