Optimal Deployment of Heterogeneous Wireless Sensor Networks Based on Improved Flower Pollination Algorithm
Xinxin Huang, Guan Li · 2023
In this paper, an improved flower pollination algorithm (IFPA) is proposed to address the problem of ineffective signal coverage across the entire area in heterogeneous wireless sensor node deployment. The algorithm presented builds upon the foundational flower pollination algorithm (FPA) and effectively tackles challenges related to sluggish convergence and susceptibility to local optima. This is achieved through the incorporation of nonlinear convergence factors and Cubic mapping techniques, which collectively contribute to its enhanced efficiency and reduced likelihood of getting trapped in local optima. Specifically, the IFPA replaces the global pollination scaling factor with a nonlinear convergence factor to improve convergence speed and better control the global search range. Additionally, the algorithm utilizes Cubic mapping technology to maintain diversity in the population during the later iterations, preventing the algorithm from falling into local optima. The experimental outcomes illustrate the superior performance of the Improved Flower Pollination Algorithm (IFPA) in optimizing the deployment of wireless sensor networks (WSNs). Notably, IFPA achieves higher coverage rates while significantly reducing network deployment costs. Therefore, the proposed algorithm has the potential for wide-ranging applications, contributing to improving wireless sensor network performance and cost reduction.