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Enhanced Secretary Bird Optimization Algorithm for Energy-Efficient Cluster Head Selection in Wireless Sensor

Ketty Siti Salamah1, Dadang Gunawan1, Ajib Setyo Arifin1

  • 1Department of Electrical Engineering, Universitas Indonesia, Depok 16424, Indonesia.

Sensors (Basel, Switzerland)
|March 14, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces an Enhanced Secretary Bird Optimization Algorithm (ESBOA) for efficient Cluster Head selection in Wireless Sensor Networks (WSNs). ESBOA improves network lifetime and energy balance by optimizing cluster formation.

Keywords:
cluster head selectionenergy-efficient clusteringmetaheuristic optimizationsecretary bird optimization algorithmwireless sensor networks

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Engineering

Background:

  • Cluster Head (CH) selection is vital for Wireless Sensor Networks (WSNs) energy balance and network lifetime.
  • Existing metaheuristic methods for CH selection face challenges like limited search diversity and premature convergence, causing uneven energy dissipation.

Purpose of the Study:

  • To address the NP-hard optimization problem of CH selection in WSNs.
  • To propose an Enhanced Secretary Bird Optimization Algorithm (ESBOA) for energy-aware CH selection.
  • To improve network lifetime and energy efficiency in WSNs.

Main Methods:

  • Formulated CH selection as a multi-criteria energy-aware optimization problem.
  • Developed ESBOA by integrating logistic chaotic map-based population initialization and an iterative local search mechanism.
  • Utilized a multi-criteria fitness function considering residual energy, distance to the base station, and node degree.
  • Implemented the framework in Python 3.11.9 using a first-order radio energy model.

Main Results:

  • ESBOA demonstrated superior performance compared to standard SBOA, CPO, and DBO.
  • The proposed method preserved more alive nodes and maintained higher residual energy.
  • ESBOA achieved approximately 3-13% improvement in last node death (LND) over standard SBOA.

Conclusions:

  • ESBOA effectively enhances energy efficiency and extends network lifetime in WSNs.
  • The integration of chaotic initialization and local search significantly improves CH selection.
  • ESBOA offers a promising solution for optimizing WSN performance.