Wireless Sensor Deployment Based on Multiobjective Adaptive Fish Migration Optimization

Abstract

Wireless Sensor Network (WSN) is a powerful tool to help humans monitor a specific area, and the deployment strategy of sensors profoundly determines the performance of WSN. How to find the best deployment method has become the research topic for many scholars. The deployment strategy aims to expand the deployment scope, reduce energy consumption, and reduce duplicate coverage areas. Many multiobjective heuristic algorithms have been proposed to solve this problem. This paper proposes a multiobjective adaptive fish migration optimization (MAFMO) algorithm, which adds an adaptive-based repository and crowding degree-selection strategy for multiobjective optimization. The simulation results reveal that the MAFMO algorithm has more advantages in malleability and distribution than other famous algorithms. Finally, the algorithm is applied to the WSN deployment problem, and the simulation results are compared with other algorithms. The results show that a better solution can be found using MAFMO.