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Novel Artificial Bee Colony Algorithms for QoS-Aware Service Selection

机译：用于QoS感知服务选择的新型人工蜂殖民地算法

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摘要

Service selection is crucial to service composition in determining the composite Quality of Service (QoS). The proliferation of composable services on the Internet and the practical need for timely delivering optimized composite solutions motivate the adoption of population-based algorithms for QoS-aware service selection. However, existing population-based algorithms are generally complicated to use, and often used as a general approach to solving different optimization problems. We propose to develop specialized algorithms for QoS-aware service selection, based on the artificial bee colony algorithm (ABC). ABC is a new and simpler implementation of swarm intelligence, which has proven to be successful in solving many real-world problems, especially the numerical optimization problems. We develop an approximate approach for the neighborhood search of ABC, which enables effective local search in the discrete space of service selection in a way that is analogical to the search in a continuous space. We present three algorithms based on the approach. All the three algorithms are designed to improve the performance and meanwhile preserve the simplicity of ABC. Each algorithm applies a different technique to leverage the unique characteristics of the service selection problem. Experimental results show higher accuracy and convergence speed of the proposed algorithms over the state of the art algorithms.
机译：服务选择对于确定服务的综合性质量（QoS）是至关重要的。可互联网上可分类服务的扩散和及时提供优化的复合解决方案的实用需求激励了采用基于人口的QoS感知服务选择的算法。然而，现有的基于人口的算法通常很复杂，并且通常用作解决不同优化问题的一般方法。我们建议基于人工蜂群算法（ABC）开发专业的QoS感知服务选择算法。 ABC是一个新的和更简单的群体智能实施，已被证明可以成功解决许多真实问题，特别是数值优化问题。我们开发了ABC的邻域搜索的近似方法，这使得能够以与在连续空间中的搜索类似于搜索的方式的方式在离散的服务选择中进行有效的本地搜索。我们基于此方法提出了三种算法。所有三种算法都旨在提高性能，同时保留ABC的简单性。每种算法都适用于不同的技术来利用服务选择问题的独特特征。实验结果表明，在最先进的算法上提出了算法的更高精度和收敛速度。

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