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Swarm optimisation algorithms applied to large balanced communication networks

机译:群优化算法应用于大型平衡通信网络

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摘要

In the last years, several combinatorial optimisation problems have arisen in the computer communications networking field. In many cases, for solving these problems it is necessary the use of meta-heuristics. An important problem in communication networks is the Terminai Assignment Probiem (TAP). Our goal is to minimise the link cost of large baianced communication networks. TAP is a NP-Hard problem. The intractability of this problem is the motivation for the pursuits of Swarm Intelligence (SI) algorithms that produce approximate, rather than exact, solutions. This paper makes a comparison among the effectiveness of three Si algorithms: Ant Colony Optimisation, Discrete Particle Swarm Optimisation and Artificial Bee Colony. We also compare the S! algorithms with severai algorithms from literature. Simulation results verify the effectiveness of the proposed algorithms. The results show that SI algorithms provide good solutions in a better running time.
机译:近年来,在计算机通信网络领域中出现了一些组合优化问题。在许多情况下,为了解决这些问题,有必要使用元启发式方法。通信网络中的一个重要问题是终端分配权限(TAP)。我们的目标是最大程度地减少大型平衡通信网络的链路成本。 TAP是NP-Hard问题。此问题的棘手问题是追求产生近似(而不是精确)解决方案的Swarm Intelligence(SI)算法的动机。本文对三种Si算法的有效性进行了比较:蚁群优化,离散粒子群优化和人工蜂群。我们还比较了S!文献中有很多算法。仿真结果验证了所提算法的有效性。结果表明,SI算法可在更好的运行时间内提供良好的解决方案。

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