IoT网络大规模全局优化算法的比较研究
Large Scale Global Optimization Algorithms for IoT Networks: A Comparative Study
摘要：
物联网（IoT）的出现通过扩展当前的互联网服务并实现机器对机器的通信而带来了通信技术的新时代。物联网的大规模部署将产生最佳功率分配的问题。优化问题的目标是获得一个可行的解决方案，最大限度地减少了无线传感器网络的总功耗，在融合中心的错误概率满足一定的标准。本文研究了无线传感器网络在高维空间中的优化问题，重点研究了分布式检测的功率分配问题。更具体地说，我们应用和比较四种算法，旨在解决大规模全局优化（LGSO）问题。这些算法分别是模因线性种群规模缩减和半参数自适应算法（MLSHADE-SPA）、基于贡献的协同进化递归差分分组算法（CBCC-RDG 3）、谱聚类差分分组差分进化协同进化算法（DGSC-DECC）和增强自适应差分进化算法（EADE）。据作者所知，这是LGSO算法首次应用于物联网网络中的最优功率分配问题。我们评估算法的性能，在几种不同的情况下，将它们应用在300，600和800维的情况下。
英文摘要：
The advent of Internet of Things (IoT) has bring a new era in communication technology by expanding the current inter-networking services and enabling the machine-to-machine communication. IoT massive deployments will create the problem of optimal power allocation. The objective of the optimization problem is to obtain a feasible solution that minimizes the total power consumption of the WSN, when the error probability at the fusion center meets certain criteria. This work studies the optimization of a wireless sensor network (WNS) at higher dimensions by focusing to the power allocation of decentralized detection. More specifically, we apply and compare four algorithms designed to tackle Large scale global optimization (LGSO) problems. These are the memetic linear population size reduction and semi-parameter adaptation (MLSHADE-SPA), the contribution-based cooperative coevolution recursive differential grouping (CBCC-RDG3), the differential grouping with spectral clustering-differential evolution cooperative coevolution (DGSC-DECC), and the enhanced adaptive differential evolution (EADE). To the best of the authors knowledge, this is the first time that LGSO algorithms are applied to the optimal power allocation problem in IoT networks. We evaluate the algorithms performance in several different cases by applying them in cases with 300, 600 and 800 dimensions.
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