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Multi-Strategy Bald Eagle Search Algorithm Embedded Orthogonal Learning for Wireless Sensor Network (WSN) Coverage

Haixu Niu^1,2, Yonghai Li², Chunyu Zhang³

¹Faculty of Information Science and Engineering, Management and Science University, Shah Alam 40100, Malaysia.

Sensors (Basel, Switzerland)

|November 9, 2024

Summary

This summary is machine-generated.

This study introduces a new algorithm, OLMBES, to optimize wireless sensor network (WSN) node placement. The enhanced algorithm improves coverage ratio and node uniformity for critical WSN applications.

Keywords:

Lévy flight bald eagle search algorithm coverage control orthogonal learning quadratic interpolation quasi-reflection-based learning wireless sensor network

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Area of Science:

Computer Science
Electrical Engineering
Optimization Algorithms

Background:

Coverage control is a critical challenge in wireless sensor network (WSN) applications.
Optimizing sensor node deployment in complex monitoring areas presents a high-dimensional problem.

Purpose of the Study:

To propose an enhanced optimization algorithm for sensor node deployment in WSNs.
To improve the efficiency and robustness of sensor node location optimization.

Main Methods:

Introduced the Orthogonal Learning Multi-Strategy Bald Eagle Search (OLMBES) algorithm.
Integrated Lévy flight, quasi-reflection-based learning, and quadratic interpolation into the Bald Eagle Search (BES) algorithm.
Incorporated orthogonal learning to enhance robustness and prevent premature convergence.

Main Results:

The OLMBES algorithm demonstrated superior performance on CEC2014 benchmark functions compared to existing methods.
Achieved a greater network coverage ratio and improved node uniformity in WSN coverage optimization.
Showcased stronger optimization stability for WSN deployment problems.

Conclusions:

The proposed OLMBES algorithm effectively optimizes sensor node deployment in WSNs.
OLMBES offers enhanced global exploration, faster convergence, and improved robustness.
The method provides a significant advancement for WSN coverage optimization challenges.