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Dynamic selection of composite Web services based on a genetic algorithm optimized new structured neural network

机译:基于遗传算法优化的新型结构化神经网络的复合Web服务动态选择

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In order to realize a high-quality and good-performance service composition, based on current approach, we propose a new QoS-driven dynamic selection of composite Web services, which takes account of both the QoS properties and interface parameters matching degree. When doing the selection, we aware that the task is more or less a multistage decision-making process. Motivated by neural networks' high parallel performance and genetic algorithm's powerful computation ability, a genetic algorithm optimized neural network algorithm is proposed in this paper for such task. In order to make this algorithm more adaptable for multistage decision-making problem, we propose a new structured neural network to express the composed service instead of using the traditional neural networks, which minimizes the neurons involved and shows high performance than the earlier ones. Finally, through experimentation one can find that method proposed in this paper is more practical and effective than others.
机译:为了实现高质量和良好性能的服务组合,基于当前方法,我们提出了一种新的QoS驱动的动态选择复合Web服务,这考虑了QoS属性和接口参数匹配程度。在进行选择时,我们意识到任务或多或少是多级决策过程。通过神经网络的高度平行性能和遗传算法的强大计算能力,在本文中提出了一种遗传算法优化神经网络算法。为了使该算法更适合多级决策问题,我们提出了一种新的结构化神经网络来表达所属的服务而不是使用传统的神经网络,这最小化了所涉及的神经元并显示出高性能的高性能。最后,通过实验可以找到本文提出的方法比其他方法更实用和有效。

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