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Multi-strategy Sea Horse Optimization algorithm for UAV path planning
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DOI：10.3389/frobt.2026.1792384.png)
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Abstract
En 中文
Unmanned aerial vehicle (UAV) path planning is a challenging constrained optimization problem and a key component of autonomous navigation. Traditional optimization techniques frequently encounter difficulties in handling the complex constraints of UAV path planning; and even metaheuristic algorithms may suffer from premature convergence to local optima. A modified variant of the Sea Horse Optimization algorithm (SHO); denoted as moSHO; is introduced for threat-aware UAV path planning. The proposed algorithm extends the original SHO’s movement; predation; and reproduction mechanisms through three cooperative strategies. First; a fish-aggregating device (FAD) mechanism promotes behavioral diversity through adaptive; range-aware perturbations. Second; a best–worst position mutation (BWPM) operator applies fine-grained Gaussian adjustments to the best-performing individuals while simultaneously guiding the worst individuals toward the current best using a differential update with Cauchy perturbation. Third; quasi–reflection-based learning (QRBL) introduces quasi-opposite candidates to strengthen exploration and population diversity. The integration of these strategies strengthens the exploration capability without reducing exploitation; resulting in a more balanced optimization process. An evaluation of 23 benchmark functions demonstrates the robustness of moSHO. Moreover; experiments on the UAV path planning model under threat environments prove its reliability in identifying safe; feasible paths.
Keywords:
metaheuristics
multi-strategy
opposite-based learning
Sea Horse Optimization
unmanned aerial vehicle path planning
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