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Related Experiment Video

Updated: Sep 12, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Augmented secretary bird optimization algorithm for wireless sensor network deployment and engineering problem.

Qingwen Meng1, Xi Kuang2, Zihan Yu3

  • 1School of Computer, Electronics and Information, Guangxi University, Nanning, Guangxi, China.

Plos One
|August 8, 2025
PubMed
Summary
This summary is machine-generated.

The enhanced Secretary Bird Optimization Algorithm (ASBOA) improves solution accuracy and convergence speed for optimization problems. It outperforms existing methods in wireless sensor network deployment and engineering design challenges.

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Metaheuristics

Background:

  • Wireless Sensor Network (WSN) deployment and engineering optimization problems require efficient algorithms.
  • Existing optimization algorithms like PSO, GWO, DBO, and CPO have limitations in accuracy and convergence speed.
  • The original Secretary Bird Optimization Algorithm (SBOA) provides a foundation for developing improved optimization techniques.

Purpose of the Study:

  • To develop an enhanced Secretary Bird Optimization Algorithm (ASBOA) with improved solution accuracy and convergence speed.
  • To address the limitations of existing algorithms in complex optimization tasks.
  • To evaluate ASBOA's performance on benchmark functions and real-world problems.

Main Methods:

  • Introduction of a differential collaborative search mechanism to avoid local optima.
  • Implementation of an optimal boundary control mechanism for enhanced convergence.
  • Utilization of an information retention control mechanism to update populations effectively.
  • Evaluation using CEC2017 and CEC2022 benchmark test functions.
  • Application to WSN deployment and engineering design problems (three-bar truss, tension/compression spring, cantilever beam).

Main Results:

  • ASBOA demonstrated superior performance on 23/30 (30D) and 26/30 (100D) CEC2017 functions, and 9/12 CEC2022 functions.
  • ASBOA exhibited faster convergence speed and greater robustness compared to other algorithms.
  • In WSN deployment, ASBOA achieved an 88.32% coverage rate, a 1.12% improvement over SBOA.
  • ASBOA consistently outperformed competing methods in engineering design problems.

Conclusions:

  • The enhanced Secretary Bird Optimization Algorithm (ASBOA) shows strong overall performance and significant potential for complex optimization problems.
  • ASBOA offers improved accuracy and faster convergence compared to SBOA and other leading algorithms.
  • Further refinement is needed for high-dimensional multimodal and constrained optimization problems to enhance stability and parameter sensitivity.