Enhanced secretary bird optimization algorithm with multi-strategy fusion and Cauchy-Gaussian crossover.

Area of Science:

• Computational Intelligence
• Metaheuristic Optimization
• Algorithm Design

Background:

• Secretary Bird Optimization Algorithm (SBOA) suffers from low convergence accuracy and local optima.
• Existing optimization algorithms often struggle with high-dimensional and complex problem spaces.

Purpose of the Study:

• To propose an improved algorithm, UTFSBOA, addressing SBOA's limitations.
• To enhance global exploration, balance exploitation, and enrich solution diversity in optimization.

Main Methods:

• Integration of multi-strategy collaboration and Cauchy-Gaussian crossover into SBOA.
• Introduction of an adaptive nonlinear factor-based directional search.
• Incorporation of an exponentially decaying energy escape factor inspired by Harris Hawk Optimization (HHO).

Main Results:

• UTFSBOA achieved 81.18% and 88.22% higher convergence accuracy than SBOA in 30D and 100D scenarios.
• Obtained optimal solutions for 7 out of 12 complex functions in the CEC2022 test set.
• Demonstrated significant improvements (up to 91.3%) on real-world engineering problems.

Conclusions:

• Multi-strategy synergy significantly enhances algorithmic robustness in high-dimensional complex spaces.
• UTFSBOA is an effective solution for constrained and discrete optimization challenges.
• The proposed enhancements overcome the limitations of the original SBOA.