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Related Experiment Video

Updated: Jan 7, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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CQLHBA: Node Coverage Optimization Using Chaotic Quantum-Inspired Leader Honey Badger Algorithm.

Xiaoliu Yang1, Mengjian Zhang2

  • 1Department of Automation Engineering, Moutai Institute, Renhuai 564507, China.

Biomimetics (Basel, Switzerland)
|December 24, 2025
PubMed
Summary
This summary is machine-generated.

A new Chaotic Quantum-Inspired Leader Honey Badger Algorithm (CQLHBA) improves Node Coverage Optimization (NCO) accuracy. This swarm intelligence algorithm offers enhanced global search and stability for wireless sensor networks.

Keywords:
bio-inspired optimizationchaotic encodinghoney badger algorithmnode coveragequantum rotationwireless sensor network

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Optimization Algorithms

Background:

  • Existing swarm intelligence (SI) algorithms for Node Coverage Optimization (NCO) suffer from limited solution accuracy.
  • Node Coverage Optimization is crucial for efficient deployment in wireless sensor networks.

Purpose of the Study:

  • To propose a novel Chaotic Quantum-Inspired Leader Honey Badger Algorithm (CQLHBA) to address the accuracy limitations in NCO.
  • To enhance the exploration and exploitation capabilities of swarm intelligence algorithms.

Main Methods:

  • Developed CQLHBA by integrating chaotic dynamics, quantum rotation, and Lévy flight strategies with the basic Honey Badger Algorithm (HBA).
  • Implemented an adjustment strategy for parameter α1 to balance follower position optimization and improve exploration.
  • Validated performance using 21 benchmark functions and compared against state-of-the-art SI algorithms.

Main Results:

  • CQLHBA demonstrated superior performance compared to other SI algorithms on benchmark functions.
  • The algorithm exhibited enhanced global search capability and robust stability.
  • Applied to NCO in wireless sensor networks (WSNs), CQLHBA achieved significant improvements in coverage rate and network connectivity.

Conclusions:

  • The proposed CQLHBA offers a significant advancement over existing SI algorithms for NCO.
  • The integration of novel strategies effectively enhances both exploration and exploitation.
  • CQLHBA shows practical efficacy and potential for real-world applications in WSNs.