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A Novel Adaptive Superb Fairy-Wren (Malurus cyaneus) Optimization Algorithm for Solving Numerical Optimization

Tianzuo Yuan1, Huanzun Zhang2, Jie Jin3

  • 1Faculty of Health Sciences, University of Macau, Macau 999078, China.

Biomimetics (Basel, Switzerland)
|August 27, 2025
PubMed
Summary
This summary is machine-generated.

The Adaptive Superb Fairy-wren Optimization Algorithm (ASFOA) enhances the original SFOA by improving adaptability and global search capabilities. ASFOA demonstrates superior performance in complex optimization problems and engineering applications.

Keywords:
adaptive switching frameworkcovariance matrixnumerical optimizationsuperb fairy-wren optimization algorithm

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Area of Science:

  • Computational Intelligence
  • Meta-heuristic Optimization
  • Swarm Intelligence

Background:

  • The Superb Fairy-wren Optimization Algorithm (SFOA) is an animal-inspired meta-heuristic. It faces limitations in complex environments, including poor adaptability, reduced population diversity, and susceptibility to local optima.
  • Existing SFOA struggles with global search ability and adapting its switching mechanism to challenging optimization problems.

Purpose of the Study:

  • To introduce an improved meta-heuristic algorithm, the Adaptive Superb Fairy-wren Optimization Algorithm (ASFOA).
  • To address the identified shortcomings of the SFOA, specifically enhancing its adaptability, population diversity, and global search capabilities for complex optimization tasks.

Main Methods:

  • The proposed ASFOA incorporates novel strategies to overcome the limitations of the original SFOA.
  • Experimental validation was conducted using the CEC2018 and CEC2022 benchmark test suites.
  • Performance was evaluated by comparing ASFOA against eight other meta-heuristic algorithms and on 10 engineering constrained optimization problems.

Main Results:

  • ASFOA demonstrated superior performance compared to existing meta-heuristics on the CEC2018 test set, achieving excellent average rankings.
  • The algorithm exhibited strong convergence and solution distribution characteristics on the CEC2022 test set, validating its robustness.
  • ASFOA achieved a low average ranking on engineering constrained optimization problems, indicating its effectiveness in real-world applications.

Conclusions:

  • The Adaptive Superb Fairy-wren Optimization Algorithm (ASFOA) is a competitive and effective variant of meta-heuristic algorithms.
  • ASFOA shows significant improvements in handling complex optimization problems, demonstrating excellent convergence and robustness.
  • The proposed ASFOA presents a promising approach for solving both theoretical and practical optimization challenges.