一种求解面向服务软件部署优化问题的多目标蚁群算法

发布时间：2018-03-30 06:04

  本文选题：面向服务软件　切入点：部署优化　出处：《中南大学学报(自然科学版)》2017年09期

【摘要】：基于根据动态变化的外部环境调整面向服务软件的部署方案是提升其运行性能、降低运行成本的一种有效途径,提出一种基于多目标蚁群算法的MACO-DO,以便在自动为面向服务软件寻找一组在性能和成本之间作出最优权衡的部署方案。MACO-DO算法是对传统多目标蚁群算法的一种改进,引入摒弃精英解策略以避免算法早熟收敛,设计1个局部搜索过程以加快获得可行解的过程。在Case 1,Case 2和Case 3共3种不同规模的模拟案例上将提出的MACO-DO算法与P-ACO算法和NSGA-Ⅱ算法进行对比。研究结果表明:MACO-DO算法在求解问题上具有更好的性能。
[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，

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