Escuela Técnica Superior de Ingenieros Industriales, Centro de Electrónica Industrial,Universidad Politécnica de Madrid, 28006 Madrid, Spain.
Facultad de Farmacia, Campus Montepríncipe, Universidad CEU San Pablo, 28668 Madrid, Spain.
Sensors (Basel). 2019 Feb 7;19(3):677. doi: 10.3390/s19030677.
During the last decade, Wireless sensor networks (WSNs) have attracted interest due to the excellent monitoring capabilities offered. However, WSNs present shortcomings, such as energy cost and reliability, which hinder real-world applications. As a solution, Relay Node (RN) deployment strategies could help to improve WSNs. This fact is known as the Relay Node Placement Problem (RNPP), which is an NP-hard optimization problem. This paper proposes to address two Multi-Objective (MO) formulations of the RNPP. The first one optimizes average energy cost and average sensitivity area. The second one optimizes the two previous objectives and network reliability. The authors propose to solve the two problems through a wide range of MO metaheuristics from the three main groups in the field: evolutionary algorithms, swarm intelligence algorithms, and trajectory algorithms. These algorithms are the Non-dominated Sorting Genetic Algorithm II (NSGA-II), Strength Pareto Evolutionary Algorithm 2 (SPEA2), Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D), Multi-Objective Artificial Bee Colony (MO-ABC), Multi-Objective Firefly Algorithm (MO-FA), Multi-Objective Gravitational Search Algorithm (MO-GSA), and Multi-Objective Variable Neighbourhood Search Algorithm (MO-VNS). The results obtained are statistically analysed to determine if there is a robust metaheuristic to be recommended for solving the RNPP independently of the number of objectives.
在过去的十年中，由于无线传感器网络 (WSN) 提供了出色的监测能力，因此引起了人们的兴趣。然而，WSN 存在一些缺点，例如能源成本和可靠性，这阻碍了其在实际中的应用。作为一种解决方案，中继节点 (RN) 的部署策略可以帮助改善 WSN。这一事实被称为中继节点放置问题 (RNPP)，它是一个 NP 难的优化问题。本文提出了两种 RNPP 的多目标 (MO) 公式。第一个公式优化了平均能量成本和平均灵敏度区域。第二个公式优化了前两个目标和网络可靠性。作者提出通过三种主要群体中的广泛的 MO 元启发式算法来解决这两个问题：进化算法、群体智能算法和轨迹算法。这些算法是基于非支配排序遗传算法 II (NSGA-II)、基于强度 Pareto 进化算法 2 (SPEA2)、基于分解的多目标进化算法 (MOEA/D)、多目标人工蜂群算法 (MO-ABC)、多目标萤火虫算法 (MO-FA)、多目标引力搜索算法 (MO-GSA) 和多目标可变邻域搜索算法 (MO-VNS)。对获得的结果进行了统计分析，以确定是否有一种稳健的元启发式算法可以推荐用于解决 RNPP，而与目标数量无关。
