An enhanced secretary bird optimization algorithm based on precise elimination mechanism and boundary control for...

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Metaheuristic Computing

Background:

• Metaheuristic algorithms struggle with complex problems due to local optima and limited adaptability.
• Existing methods often require complex modeling and exhibit poor convergence.
• Enhancing exploration and exploitation is crucial for robust optimization.

Purpose of the Study:

• To propose an enhanced Secretary Bird Optimization Algorithm (MESBOA) for improved performance on complex optimization tasks.
• To address limitations of traditional metaheuristics, including local optima and premature convergence.
• To validate MESBOA's effectiveness through benchmark functions and a practical image enhancement application.

Main Methods:

• Developed MESBOA integrating a precise population elimination strategy for population optimization.
• Implemented a lens imaging-based opposition learning strategy to expand solution space exploration.
• Incorporated a boundary control strategy based on the best individual to constrain the search range.

Main Results:

• MESBOA significantly outperformed the original SBOA and other algorithms (GWO, WOA, PSO) on 23 benchmark functions and the CEC2022 test suite.
• Achieved superior convergence speed, solution accuracy, and stability compared to existing methods.
• Demonstrated enhanced performance in low-light image enhancement, improving MSE, PSNR, and SSIM metrics.

Conclusions:

• MESBOA offers an efficient and robust solution for complex optimization problems.
• The proposed enhancements effectively mitigate local optima and premature convergence.
• MESBOA shows significant potential for broad application across various scientific and engineering fields.