云制造环境下的知识服务组合优化策略

Optimization strategy of knowledge service composition in cloud manufacturing environment

针对云制造环境下知识服务组合优化问题,通过分析服务组合优化过程,采用服务质量感知的服务组合策略建立了以时间、成本、可用性、准确性、创新性、可信性为优化目标的服务组合优化模型;采用聚类分析及关联规则挖掘策略对搜索空间进行预处理,减小了搜索空间,实现了知识服务资源的快速精准定位与匹配,提高了知识服务组合的效率和成功率;针对标准涡流搜索算法易陷入局部最小的问题,引入多涡流中心搜索及涡流中心自适应更新策略,提出一种改进的多中心涡流搜索算法对服务组合问题进行全局优化。仿真实验表明,聚类分析及关联规则挖掘策略与多中心涡流搜索算法结合,能极大地缩短寻优时间并获得更优解,从而更有效地解决知识服务组合优化问题。

Aiming at the optimization problem of knowledge service composition under cloud manufacturing,a service composition and optimization model with time,cost,availability,accuracy,innovation and credibility as the optimization objective was established with the the strategy of QoS-aware service composition by analyzing the process of service composition and optimization.The search space was pre-processed by using clustering analyse and association rule mining strategy,which contributed to reducing the search space and achieving identifying and matching of knowledge service resources quickly and accurately,and improving the efficiency and success rate of knowledge service composition.Aiming at the problem that standard vortex search algorithm is easy to fall into local minimum,an improved mutiple centre vortex search algorithm was proposed for global optimization of service composition problem.Simulation results showed that the combination of clustering analyse and association rule mining strategy with multi center vortex search algorithm could greatly shorten the search time and get better solution,and can solve the optimization problem of knowledge service composition more effectively.