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The aim is to enhance the coverage optimization of wireless sensor networks by improving the Northern Goshawk Optimization algorithm.

June 3, 2026BiomimeticsOpen Access

Coverage Optimization Strategy for Wireless Sensor Networks Based on Improved Northern Goshawk Optimization Algorithm

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Randomized trial demonstrates improved coverage in wireless sensor networks, suggesting an efficient deployment method.

The aim is to enhance the coverage optimization of wireless sensor networks by improving the Northern Goshawk Optimization algorithm.
Improved Northern Goshawk Optimization algorithm integrates a Logistic chaotic map for initialization.
Introduced nonlinear dynamic weight for balancing exploration and exploitation.
Applied Gaussian–Lévy hybrid mutation mechanism for escaping from local optima.
Achieved a best coverage rate of 98.32% in 2D, 7.72 percentage points higher than NGO.
In 3D, the best coverage rate reached 72.32%, 6.78 percentage points higher than NGO.
INGO shows smoother convergence in the late iteration stage with significant robustness improvements.

Wang et al. (2026) studied this question.

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