Multi-strategy Sea Horse Optimization algorithm for UAV path planning

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.