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

TL;DR

This paper introduces GLNWOA, an enhanced whale optimization algorithm incorporating a log-normal distribution to improve convergence and diversity, achieving superior coverage optimization in wireless sensor networks.

Contribution

The paper presents a novel enhancement to WOA by integrating log-normal distribution, leader guidance, and spiral updating to improve optimization performance.

Findings

Achieved 99.0013% coverage with 25 nodes in a 60m x 60m area.

Outperformed existing algorithms like AROA, HHO, and WOABAT.

Demonstrated faster convergence and higher stability in tests.

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.