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An Enhanced Secretary Bird Optimization Algorithm Based on Multi Population Management for Numerical Optimization

Jin Zhu1, Bojun Liu2, Jun Zheng3

  • 1School of Journalism and Communication, Tsinghua University, Beijing 100000, China.

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Summary
This summary is machine-generated.

This study introduces the Multi-population Experience-trend guided Secretary Bird Optimization Algorithm (MESBOA) to enhance swarm-based optimization. MESBOA overcomes limitations of the original algorithm, demonstrating superior performance in accuracy and convergence speed.

Keywords:
benchmark test suiteexperience trend guidingmulti-population managementsecretary bird optimization algorithmswarm intelligence

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Area of Science:

  • Computational Intelligence
  • Swarm Intelligence
  • Optimization Algorithms

Background:

  • The Secretary Bird Optimization Algorithm (SBOA) is a novel meta-heuristic inspired by avian behavior.
  • Existing SBOA exhibits challenges with exploration-exploitation balance, population diversity, and local optima.
  • These limitations hinder its effectiveness in complex optimization tasks.

Purpose of the Study:

  • To propose an enhanced Secretary Bird Optimization Algorithm (MESBOA).
  • To address the drawbacks of the original SBOA, including unbalanced exploration-exploitation and premature convergence.
  • To improve the algorithm's performance on benchmark and real-world optimization problems.

Main Methods:

  • Integration of a multi-population management strategy into SBOA.
  • Incorporation of an experience-trend guidance strategy to direct the search process.
  • Comparative analysis against eight advanced algorithms on CEC2017 and CEC2022 test suites.

Main Results:

  • MESBOA achieved superior performance across various dimensions (10-D, 20-D, 50-D, 100-D) on CEC test suites.
  • Demonstrated faster convergence, enhanced robustness, and higher accuracy compared to existing algorithms.
  • Validated applicability to real-world engineering constrained optimization problems.

Conclusions:

  • MESBOA effectively overcomes the limitations of the standard SBOA.
  • The proposed enhancements lead to significant improvements in optimization efficiency and solution quality.
  • MESBOA shows strong potential for practical applications in complex optimization scenarios.