Multi-Strategy Variable Secretary Bird Optimization Algorithm (MSVSBOA) for Global Optimization and UAV 3D Path Planning

Amir Seyyedabbasi

doi:10.3390/sym18020273

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Multi-Strategy Variable Secretary Bird Optimization Algorithm (MSVSBOA) for Global Optimization and UAV 3D Path Planning

2026-01-31

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Amir Seyyedabbasi *

DOI：10.3390/sym18020273

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Abstract

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In this study, an enhanced variant of the Secretary Bird Optimization Algorithm (SBOA), named MSVSBOA, is proposed to address the limitations of the SBOA in global optimization and UAV 3D path-planning. The proposed MSVSBOA integrates three complementary strategies to achieve a balanced exploration and exploitation trade-off. First, a Levy-based Directed Exploration mechanism is introduced to enrich the global search capability and prevent premature convergence. Second, a spiral movement mechanism is incorporated to strengthen the local exploitation behavior and improve convergence accuracy. Third, a Differential Evolution-inspired refinement strategy (DE-Refinement) is employed to accelerate fine-grained exploitation during the later stages of optimization. The performance of the MSVSBOA is extensively evaluated on the CEC 2014 and CEC 2022 benchmark suites. Experimental results demonstrate that the MSVSBOA achieves superior accuracy, faster convergence, and improved robustness compared to the SBOA and other multi-strategy variants. Furthermore, the MSVSBOA is applied to a challenging UAV 3D path planning problem, where it successfully generates safe, smooth, and collision-free trajectories while outperforming competing algorithms. These findings confirm the effectiveness of the proposed MSVSBOA for both global optimization problems and real-world UAV applications.

Keywords:

secretary bird optimization

multi-strategy

Levy flight

spiral

differential evolution