IAOO: An Improved Animated Oat Optimization Algorithm with Adaptive Multi-Strategy Search for UAV Path Planning

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.