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首页> 外文期刊>International Journal of Computer Integrated Manufacturing >An approach for service composition optimisation considering service correlation via a parallel max-min ant system based on the case library
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An approach for service composition optimisation considering service correlation via a parallel max-min ant system based on the case library

机译：基于案例库的并行MAX-MIN ANT系统考虑服务相关性的服务成分优化方法

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摘要

With the rapid development of cloud manufacturing, service composition optimisation (SCO) has become an important topic recently. Since the quality of service (QoS) varies widely in different service compositions due to the problem of service correlation, many SCO-based optimisation algorithms have been recently proposed to obtain a better service composition with an optimal QoS by combining it with the correlation-aware model. However, most existing approaches either consider the service correlation problem inadequately or suffer from a low efficiency of the optimisation algorithm. To address this problem, a novel optimisation algorithm named the parallel max-min ant system based on the case library (PMMAS-CL) is proposed, in which a comprehensive QoS correlation model is introduced with full consideration of the service correlation. In the PMMAS-CL algorithm, another special ant is employed to maintain the diversity of the population, and then a local learning strategy is adopted simultaneously to accelerate the convergence rate. Moreover, the case library, enhanced with an autonomous learning mechanism, is also applied to further improve the searching efficiency for the SCO problem. The experimental results show that the model significantly outperforms the previous approaches, and the PMMAS-CL algorithm can find the global optimal solution effectively compared with other state-of-the-art approaches.
机译：随着云制造的快速发展，服务成分优化（SCO）最近已成为一个重要的主题。由于服务质量（QoS）由于服务相关问题而在不同的服务组合中差异，最近已经提出了许多基于SCO的优化算法，以通过将其与相关感知结合来获得具有最佳QoS的更好的服务组成模型。然而，大多数现有方法要么不充分考虑服务相关问题或遭受优化算法的低效率。为了解决这个问题，提出了一种基于案例库（PMMAS-CL）的并行MAX-MIN蚁蚁系统的新颖优化算法，其中通过充分考虑服务相关性来引入全面的QoS相关模型。在PMMAS-CL算法中，采用另一个特殊蚂蚁来维持群体的多样性，然后同时采用局部学习策略以加速收敛速度。此外，还应用了具有自主学习机制的案例库，也应用于进一步提高SCO问题的搜索效率。实验结果表明，该模型显着优于先前的方法，与其他最先进的方法相比，PMMS-CL算法可以有效地找到全球最优解决方案。

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