[Paper] Multi-strategy Improved Northern Goshawk Optimization for WSN Coverage Enhancement

Source: arXiv - 2601.01898v1

Overview

The paper introduces a multi‑strategy version of the Northern Goshawk Optimization (NGO) algorithm aimed at boosting coverage and connectivity in Wireless Sensor Networks (WSNs). By weaving together chaotic initialization and a bidirectional evolutionary dynamic, the authors achieve markedly higher sensor deployment quality than existing meta‑heuristic approaches.

Key Contributions

• Chaotic population initialization using multivariate chaotic maps to ensure a more uniform and diverse starting set of solutions.
• Bidirectional evolutionary dynamics applied after the classic pursuit‑and‑evasion phase, which simultaneously explores and exploits the search space to escape local optima.
• Integrated multi‑strategy NGO framework that combines the above mechanisms into a single optimizer tailored for WSN coverage problems.
• Comprehensive simulation study comparing the proposed algorithm against several state‑of‑the‑art benchmarks (e.g., PSO, GA, standard NGO), showing superior coverage ratios and node connectivity.

Methodology

1. Problem formulation – The WSN coverage task is cast as a continuous optimization problem: each sensor’s coordinates are decision variables, and the objective function balances coverage area (maximizing the region sensed) with connectivity (ensuring sensors can communicate).
2. Baseline NGO – Inspired by the hunting behavior of northern goshawks, the original NGO alternates between a pursuit phase (exploration) and an evasion phase (exploitation).
3. Multi‑strategy enhancements
   • Multivariate chaotic mapping: Instead of random uniform sampling, the initial population is generated via a chaotic map (e.g., Logistic‑type or Tent map extended to multiple dimensions). Chaotic sequences have better ergodicity, giving a more diverse set of initial sensor placements.
   • Bidirectional population evolutionary dynamics: After the pursuit‑evasion step, the algorithm splits the swarm into two sub‑populations that evolve in opposite directions—one intensifies exploitation around the current best, while the other expands exploration toward less‑visited regions. Periodic exchange of individuals maintains diversity.
4. Fitness evaluation – For each candidate deployment, the algorithm computes (a) the percentage of the target area covered by the sensors’ sensing radii, and (b) a connectivity metric based on the network graph’s average node degree. A weighted sum forms the final fitness score.
5. Termination – The process repeats until a maximum number of iterations or a convergence threshold is reached.

Results & Findings

• Coverage improvement: The multi‑strategy NGO achieved up to 12 % higher coverage than standard NGO and 7–10 % over classic PSO/GA baselines across varied network sizes (30–100 nodes).
• Connectivity gains: The proposed method maintained average node degree within the desired range, reducing isolated nodes by ≈15 % compared to benchmarks.
• Convergence speed: Thanks to chaotic initialization and bidirectional dynamics, the algorithm converged to near‑optimal solutions 30–40 % faster (fewer iterations) than the vanilla NGO.
• Robustness: Across 30 independent runs per scenario, the variance in final fitness was lower, indicating more stable performance.

Practical Implications

• Deployment planning tools – Engineers can embed the multi‑strategy NGO into GIS‑based sensor placement software to automatically generate high‑coverage layouts for smart‑city, environmental monitoring, or industrial IoT deployments.
• Energy efficiency – Better coverage with fewer sensors translates to lower hardware costs and reduced power consumption, extending battery life in remote or hard‑to‑reach installations.
• Dynamic re‑configuration – The algorithm’s fast convergence makes it suitable for online re‑optimization when nodes fail or the monitored area changes (e.g., disaster response scenarios).
• Cross‑domain applicability – The same multi‑strategy framework can be adapted to other spatial optimization problems such as UAV path planning, base‑station placement in 5G/6G networks, or facility location in logistics.

Limitations & Future Work

• Scalability to massive networks: Experiments capped at 100 nodes; performance on thousands of sensors (e.g., city‑scale IoT) remains to be validated.
• Real‑world constraints: The current model assumes ideal circular sensing ranges and unobstructed communication; incorporating terrain, obstacles, and heterogeneous sensor capabilities would increase realism.
• Hybridization opportunities: The authors suggest exploring combinations with machine‑learning‑based surrogate models to further reduce evaluation cost, especially for complex, multi‑objective formulations.

Bottom line: By marrying chaotic initialization with a bidirectional evolutionary scheme, the authors deliver a more robust and efficient NGO variant that can materially improve WSN coverage—an advance that developers building large‑scale sensor infrastructures should keep on their radar.

Authors

• Yiran Tian
• Yuanjia Liu

Paper Information

• arXiv ID: 2601.01898v1
• Categories: cs.NE
• Published: January 5, 2026
• PDF: Download PDF