The snow ablation optimizer (SAO) is a meta-heuristic technique used to seek the best solution for sophisticated problems. In response to the defects in the SAO algorithm, which has poor search efficiency and is prone to getting trapped in local optima, this article suggests a multi-strategy improved (MISAO) snow ablation optimizer. It is employed in the unmanned aerial vehicle (UAV) path planning issue. To begin with, the tent chaos and elite reverse learning initialization strategies are merged to extend the diversity of the population; secondly, a greedy selection method is deployed to retain superior alternative solutions for the upcoming iteration; then, the Harris hawk (HHO) strategy is introduced to enhance the exploitation capability, which prevents trapping in partial ideals; finally, the red-tailed hawk (RTH) is adopted to perform the global exploration, which, enhances global optimization capability. To comprehensively evaluate MISAO’s optimization capability, a battery of digital optimization investigations is executed using 23 test functions, and the results of the comparative analysis show that the suggested algorithm has high solving accuracy and convergence velocity. Finally, the effectiveness and feasibility of the optimization path of the MISAO algorithm are demonstrated in the UAV path planning project.

The recently proposed Animated Oat Optimization (AOO) algorithm exhibits competitive search behavior, but its fixed branching rules and limited use of inter-individual information may cause diversity loss and premature stagnation. This study proposes an Improved Animated Oat Optimization algorithm (IAOO) that integrates the original AOO operator, a hybrid DE/rand/1–DE/best/1 operator with a decreasing scale factor, and an elite neighborhood-directed local search within a feedback-driven framework. Strategy probabilities are updated according to normalized successful fitness gains, enabling search effort to adapt to the current optimization state. IAOO was evaluated through 30 independent runs on the CEC2017 (dim = 30/100), CEC2020, and CEC2022 suites and achieved Friedman mean ranks of 1.50, 1.37, 2.70, and 1.83, respectively, achieving competitive Friedman mean ranks among the compared algorithms and demonstrating statistically supported advantages on most benchmark suites. In three-dimensional UAV reference-path planning, IAOO reduced the mean path cost from 406.26 for AOO to 298.94, corresponding to a 26.4% reduction, while the standard deviation decreased from 67.01 to 40.73. Its runtime increased only from 26.99 s to 27.13 s. These results indicate that feedback-based operator cooperation improves solution quality and robustness with limited computational overhead. The current UAV model produces geometrically feasible and kinematically constrained reference paths; full six-degree-of-freedom tracking validation remains future work.