An Enhanced Whale Optimization Algorithm with Log-Normal Distribution for Optimizing Coverage of Wireless Sensor Networks

TL;DR

This work tackles the challenge of premature convergence and imbalanced exploration in Whale Optimization Algorithm (WOA) for Wireless Sensor Network (WSN) coverage optimization. It introduces GLNWOA, which integrates log-normal perturbations, Good Nodes Set initialization, Leader Cognitive Guidance, and Dynamic Spiral Convergence to enhance convergence speed, diversity, and robustness. Benchmark tests show GLNWOA outperforms several state-of-the-art metaheuristics, and WSN simulations demonstrate near-complete coverage (99.0013%) in a 60x60 m area with 25 nodes, surpassing competing methods by meaningful margins. The approach offers a practical, scalable optimization framework for intelligent deployment and energy-efficient WSN operation.

Abstract

Wireless Sensor Networks (WSNs) are essential for monitoring and communication in complex environments, where coverage optimization directly affects performance and energy efficiency. However, traditional algorithms such as the Whale Optimization Algorithm (WOA) often suffer from limited exploration and premature convergence. To overcome these issues, this paper proposes an enhanced WOA which is called GLNWOA. GLNWOA integrates a log-normal distribution model into WOA to improve convergence dynamics and search diversity. GLNWOA employs a Good Nodes Set initialization for uniform population distribution, a Leader Cognitive Guidance Mechanism for efficient information sharing, and an Enhanced Spiral Updating Strategy to balance global exploration and local exploitation. Tests on benchmark functions verify its superior convergence accuracy and robustness. In WSN coverage optimization, deploying 25 nodes in a 60 m $\times$ 60 m area achieved a 99.0013\% coverage rate, outperforming AROA, WOA, HHO, ROA, and WOABAT by up to 15.5\%. These results demonstrate that GLNWOA offers fast convergence, high stability, and excellent optimization capability for intelligent network deployment.

Figures (7)

• Figure 1: The variation of the convergence factor $a$ with iterations.
• Figure 2: Comparison of different initialization methods. (a) Left: population generated by pseudo-random initialization with $N$=150; (b) Right: population generated by Good Nodes Set initialization with $N$=150
• Figure 3: The original convergence factor $a$ and the proposed $a$
• Figure 4: Iteration curves of the algorithms
• Figure 5: Random distribution of wireless sensor networks.