Hybrid multi-objective evolutionary algorithms based on decomposition for wireless sensor network coverage optimization

Abstract

In Wireless Sensor Networks (WSN), maintaining a high coverage and extending the network lifetime are two conflicting crucial issues considered by real world service providers. In this paper, we consider the coverage optimization problem in WSN with three objectives to strike the balance between network lifetime and coverage. These include minimizing the energy consumption, maximizing the coverage rate and maximizing the equilibrium of energy consumption. Two improved hybrid multi-objective evolutionary algorithms, namely Hybrid-MOEA/D-I and Hybrid-MOEA/D-II, have been proposed. Based on the well-known MOEA/D algorithm, Hybrid-MOEA/D -I hybrids a genetic algorithm and a differential evolutionary algorithm to effectively optimize sub-problems of the multi-objective optimization problem in WSN. By integrating a discrete particle swarm algorithm, we further enhance solutions generated by Hybrid-MOEA/D-I in a new Hybrid-MOEA/D-II algorithm. Simulation results show that the proposed Hybrid-MOEA/D-I and Hybrid-MOEA/D-II algorithms have a significantly better performance compared with existing algorithms in the literature in terms of all the objectives concerned.

Optimum bi-level hierarchical clustering for wireless mobile tracking systems

“…HCC consists of two main sub-processes, the first is the Tree Discovery process and the second is the Cluster Formation process. Aiming to balance network lifetime and coverage in WSN, Xu et al [13] pursued three optimization objectives: minimum energy consumption, maximum coverage rate, and maximum equity of energy consumption. Two multi-objective evolutionary algorithms (MOEAs) were developed to solve the optimization model, one based on decomposition and the other based on genetic algorithms.…”

A Survey of Multiobjective Evolutionary Algorithms Based on Decomposition: Variants, Challenges and Future Directions

“…Thanks to the inherent advantages in terms of decomposition and parallelism, MOEA/D has been widely used to solve many practical engineering optimization problems, including: 1) machine learning problems, most of which can be attributed to optimization problems, and sometimes have more than one objective to optimize, such as fuzzy classifier design [4], optimization of deep neural network connection structure [5] and determination of a hyper-parameter for the regularization term of convolutional neural network [6]; 2) scheduling problems, allocating resources to different tasks in real time under certain constraints, such as hybrid flow shop scheduling [7], job shop scheduling [8], and order scheduling [9]; 3) design problems, usually achieving the goals of low cost, low consumption, and large profit while meeting the needs of users, such as wireless sensor network coverage design [10] and resource optimization for network function virtualization (NFV) requests [11]; 4) control problems, addressing the optimal parameters and mechanism in such a system as the Internet of Things [12] or reservoir flood control [13]; 5) operation problems, studying the cost reduction in the operation and maintenance of a system like hybrid energy systems [14]; and 6) investment problems, considering how to invest limited funds in a number of projects to maximize returns like portfolio optimization problem [15].…”

Wireless Sensor Network Deployment of 3D Surface Based on Enhanced Grey Wolf Optimizer

“…(3D) deployment, has been studied extensively [11]- [13]. On the other hand, 3D deployment of WSNs is divided into two types, one is deployed in a 3D space [4], [9], and the other is deployed on a surface [16]- [18].…”