Work

Multi-Strategy Improved Northern Goshawk Optimization for Wireless Sensor Network Coverage Enhancement

Year: 2026

Type: article

Abstract: To address node redundancy and coverage holes in Wireless Sensor Network (WSN) deployment, this paper proposes an Improved Northern Goshawk Optimization (INGO) algorithm with multiple enhancements. It integrates a Diverse Chaotic Map Initialization Strategy (DCMIS) into the standard Northern Goshawk Optimization (NGO) for Diverse, uniform initial populations and improved global exploration. A Bidirectional Population Evolution Dynamics (BPED) mechanism follows the pursuit-and-evasion phase, applying asymmetric logic—elite guidance and selective replacement of weak individuals—to escape local optima and accelerate global convergence. Simulations reveal uniform grid topologies and an average coverage ratio of 91.90% with INGO, outperforming Northern Goshawk Optimization (NGO), Artificial Bee Colony (ABC), Improved Wild Horse Optimizer (IWHO), and the Firefly Algorithm (FA). INGO also achieves 100.00% connectivity, eliminating isolated nodes and ensuring reliable full-network communication. These results indicate that INGO achieves higher coverage and full connectivity under the studied simulation setting, demonstrating its effectiveness for WSN deployment optimization.

Source: Mathematical and Computational Applications

DOI: 10.3390/mca31030071

Authors: Yiran Tian, Yuanjia Liu

Institutions: Hebei Normal University, Xi'an University of Technology

Language: English

Topic: Metaheuristic Optimization Algorithms Research

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $1,689