CQLHBA: Node Coverage Optimization Using Chaotic Quantum-Inspired Leader Honey Badger Algorithm.

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.