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首页> 外文期刊>Applied Intelligence: The International Journal of Artificial Intelligence, Neural Networks, and Complex Problem-Solving Technologies >Discrete particle swarm optimization algorithms for two variants of the static data segment location problem
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Discrete particle swarm optimization algorithms for two variants of the static data segment location problem

机译:静态数据段位置问题的两个变体的离散粒子群优化算法

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摘要

We consider the static data segment location problem in information networks. This problem was introduced by Sen et al. (Comput Oper Res, 62:282-295 2015). We consider the problem of optimally locating large volumes of digital content that is accessed via a distributed network. A database is pre-partitioned into multiple segments and the problem is one of placing these segments at servers located in different regions. We need to jointly consider four specific subproblems: (1) the problem of locating servers in the network, (2) the problem of allocating specific data segments to each of the servers, (3) the problem of assigning users to the servers based on their query patterns, and, (4) routing queries through the network. We consider two variants of this problem depending on the topology of the network through which the servers are connected: a mesh topology and a tree topology. In this paper, we develop a solution approach based on a discrete particle swarm optimization approach. We demonstrate the superiority of our approach by comparing its performance against solutions to benchmark instances obtained previously using a simulated annealing approach (Networks, 68(1):4-22 2016b).
机译:我们考虑信息网络中的静态数据段位置问题。 Sen等人介绍了这个问题。 (计算oper Res,62:282-295 2015)。我们考虑最佳地定位通过分布式网络访问的大量数字内容的问题。数据库预先划分为多个段,问题是将这些段放置在位于不同区域的服务器中的一个。我们需要共同考虑四个特定的子问题:(1)在网络中定位服务器的问题,(2)将特定数据段分配给每个服务器的问题,(3)基于的服务器分配给服务器的问题它们的查询模式,和(4)通过网络路由查询。我们根据服务器连接的网络拓扑,我们考虑了这个问题的两个变体:网格拓扑和树拓扑。在本文中,我们开发了一种基于离散粒子群优化方法的解决方案方法。我们通过将其对先前使用模拟退火方法(网络,68(1):4-22 2016B)进行比较来证明我们对先前获得的基准实例的解决方案的性能来证明我们的方法的优越性。

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