The Animated Oat Optimization Algorithm: A nature-inspired metaheuristic for engineering optimization and a case study on Wireless Sensor Networks

In this paper, a novel metaheuristic algorithm called the Animated Oat Optimization Algorithm (AOO) is proposed, inspired by the natural behavior of Animated Oat in the environment. AOO simulates 3 unique behaviors of Animated Oat: (i) seed dispersal through natural elements such as wind, water, and animals; (ii) under the influence of hygroscopic movement, the primary awn of Animated Oat seeds undergoes distortion and rotation, enabling the entire seed to roll and propagate; and (iii) during the rolling propagation, energy is stored upon encountering obstacles, triggering a propulsion mechanism under certain conditions to further disperse the seeds. To evaluate the algorithm's capabilities in exploration and exploitation, we utilized the CEC2022 test suite, which comprises 12 functions. Comparative analysis with 9 well-known optimization algorithms demonstrates that AOO exhibits superior competitiveness. Furthermore, we extend our evaluation to five widely-used engineering design problems to confirm the algorithm's performance in these domains. Finally, we combined AOO with DV-Hop to validate its competitiveness and effectiveness in experiments on node localization in 3-dimensional Wireless Sensor Networks. These results demonstrate AOO's ability to tackle complex optimization challenges and its potential as a reliable optimizer for practical engineering applications. Source codes of AOA are publicly available at https://github.com/robingit77/Animated-Oat-Optimization-Algorithm-AOO- and https://seyedalimirjalili.com/morealgorithms.