A Dual-Mechanism Enhanced Secretary Bird Optimization Algorithm and Its Application in Engineering Optimization

Area of Science:

• Artificial Intelligence
• Computational Intelligence
• Optimization Algorithms

Background:

• The secretary bird optimization algorithm (SBOA) is a novel swarm intelligence technique.
• Existing SBOA variants face challenges with limited global exploration and local exploitation capabilities.
• Complex nonlinear optimization problems require robust and efficient solution methods.

Purpose of the Study:

• To enhance the secretary bird optimization algorithm (SBOA) by addressing its exploration and exploitation limitations.
• To introduce an improved variant named ORSBOA.
• To validate the effectiveness of ORSBOA on benchmark test suites and engineering design problems.

Main Methods:

• Developed ORSBOA by integrating an optimal neighborhood perturbation mechanism and a reverse learning strategy into the SBOA framework.
• Evaluated ORSBOA performance on the CEC2019 and CEC2022 benchmark suites.
• Tested ORSBOA on four classical engineering design problems.

Main Results:

• ORSBOA exhibited faster convergence rates compared to existing algorithms.
• The enhanced algorithm demonstrated superior robustness in solving optimization tasks.
• ORSBOA achieved higher quality solutions across various benchmark and engineering problems.
• Statistical analyses confirmed the significant improvements offered by ORSBOA.

Conclusions:

• The proposed ORSBOA effectively overcomes the limitations of the original SBOA.
• ORSBOA shows significant advantages in terms of speed, robustness, and solution quality.
• The enhanced algorithm is a promising tool for tackling complex nonlinear optimization challenges.