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

## TL;DR

This paper introduces an improved optimization algorithm that performs better on complex engineering problems.

## Contribution

A new optimization algorithm, ORSBOA, is proposed with dual mechanisms for better exploration and exploitation.

## Key findings

- ORSBOA outperforms nine state-of-the-art algorithms in convergence and solution quality.
- Statistical analysis confirms the robustness and effectiveness of ORSBOA on benchmark and engineering problems.

## Abstract

The secretary bird optimization algorithm is a recently developed swarm intelligence method with potential for solving nonlinear and complex optimization problems. However, its performance is constrained by limited global exploration and insufficient local exploitation. To address these issues, an enhanced variant, ORSBOA, is proposed by integrating an optimal neighborhood perturbation mechanism with a reverse learning strategy. The algorithm is evaluated on the CEC2019 and CEC2022 benchmark suites as well as four classical engineering design problems. Experimental results demonstrate that ORSBOA achieves faster convergence, stronger robustness, and higher solution quality than nine state-of-the-art algorithms. Statistical analyses further confirm the significance of these improvements, validating the effectiveness and applicability of ORSBOA in solving complex optimization tasks.