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Improved decomposition-based multi-objective cuckoo search algorithm for spectrum allocation in cognitive vehicular network

机译：基于分解的多目标咕咕搜索算法，用于认知车辆网络中的频谱分配

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摘要

The allocation of spectrum resources efficiently and equitably in dynamic cognitive vehicular networks is more challenging than static cognitive networks. Currently, most spectrum allocation algorithms are on the basis of a fixed network topology, thereby ignoring the mobility of cognitive vehicular users (CVUs), timeliness of licensed channels, and uncertainty of spectrum sensing in complex environments. In this paper, a cognitive vehicular network spectrum allocation model for maximizing the network throughput and fairness is established considering these factors. A rapid convergence, improved performance algorithm for solving this multi-objective problem is necessary to adapt to a dynamic network environment. Therefore, an improved decomposition-based multi-objective cuckoo search (MOICS/D) algorithm is proposed. This algorithm integrates a decomposition-based multi-objective optimization framework and an improved CS algorithm. The multi-objective problem is decomposed into multiple scalar sub-problems with different weight coefficients, and the cuckoo algorithm with adaptive steps is used to optimize these sub-problems simultaneously. Simulation results show that the MOICS/D algorithm has faster and more stable convergence than the MOEA/D and NSGA-II algorithms and can improve the throughput and fairness of the network. (c) 2018 Elsevier B.V. All rights reserved.
机译：在动态认知车辆网络中有效且公平地提供频谱资源的分配比静态认知网络更具挑战性。目前，大多数频谱分配算法是在固定网络拓扑的基础上，从而忽略认知车辆用户（CVU）的移动性，许可信道的时间，以及复杂环境中的光谱感测的不确定性。在本文中，考虑了这些因素，建立了一种用于最大化网络吞吐量和公平性的认知车辆网络频谱分配模型。快速收敛，改进的解决该多目标问题的性能算法是适应动态网络环境所必需的。因此，提出了一种改进的基于分解的多目标Cuckoo搜索（MOICS / D）算法。该算法集成了基于分解的多目标优化框架和改进的CS算法。多目标问题被分解成具有不同权重系数的多个标量子问题，并且使用自适应步骤的咕咕算法用于同时优化这些子问题。仿真结果表明，摩尔/ D算法的收敛速度比MOEA / D和NSGA-II算法更快，更稳定，可以提高网络的吞吐量和公平性。 （c）2018年elestvier b.v.保留所有权利。

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