A Fusion Multi-Strategy Gray Wolf Optimizer for Enhanced Coverage Optimization in Wireless Sensor Networks.

Area of Science:

• Computer Science
• Optimization Algorithms
• Wireless Sensor Networks

Background:

• Wireless Sensor Networks (WSNs) are crucial for IoT, smart cities, and environmental monitoring.
• Optimizing WSN coverage is vital for efficacy under resource constraints.
• Existing methods face challenges like low efficiency, high computational cost, and local optima convergence.

Purpose of the Study:

• To develop an advanced optimization algorithm for WSN coverage.
• To address the limitations of conventional WSN optimization approaches.
• To enhance global coverage efficiency and reduce computational overhead in WSN deployment.

Main Methods:

• Proposes the Fusion Multi-Strategy Gray Wolf Optimizer (FMGWO), an enhanced Gray Wolf Optimizer (GWO).
• Integrates electrostatic field initialization, dynamic parameter adjustment, an elder council mechanism, alpha wolf rotation, and a hybrid mutation strategy.
• Employs ablation studies to validate individual strategy contributions and simulation experiments for performance evaluation.

Main Results:

• FMGWO demonstrates superior performance in WSN coverage optimization compared to PSO, GWO, CSA, DE, GA, FA, OGWO, DGWO1, and DGWO2.
• Achieves up to 98.63% coverage with only 30 nodes, significantly outperforming existing algorithms.
• Exhibits improved convergence speed, enhanced stability, and greater global search capability.

Conclusions:

• FMGWO is a highly effective solution for efficient WSN deployment and coverage optimization.
• Offers significant advantages for resource-constrained optimization problems in IoT and edge computing.
• The proposed strategies collectively enhance population distribution, vitality, and global search performance.